# ==============================
# ENVIRONMENT SETUP
# ==============================
import os
os.environ["KERAS_BACKEND"] = "tensorflow"  # Keep if swapping to Keras 3 later

# ==============================
# STANDARD LIBRARIES
# ==============================
import time
import random
import string
import datetime
from collections import defaultdict

# ==============================
# CORE SCIENTIFIC STACK
# ==============================
import numpy as np
import pandas as pd
from scipy.linalg import sqrtm

# ==============================
# VISUALIZATION
# ==============================
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns

# Optional: model diagrams
try:
    import visualkeras
except ImportError:
    visualkeras = None

# ==============================
# PROGRESS / DISPLAY / I/O
# ==============================
from tqdm import tqdm
from IPython import display
import imageio.v2 as imageio  # Avoids deprecation warnings

# ==============================
# TENSORFLOW ECOSYSTEM
# ==============================
import tensorflow as tf
# Optional: Addons
try:
    import tensorflow_addons as tfa
except ImportError:
    tfa = None

# ---- tf.keras: layers / models / utils
from tensorflow.keras.layers import (
    Dense, Reshape, Flatten, Dropout, BatchNormalization, LeakyReLU,
    Conv2DTranspose, Conv2D, Embedding, Concatenate, Input,
    ReLU, Activation, ZeroPadding2D, Add, GlobalAveragePooling2D,
    MaxPooling2D, UpSampling2D, Lambda
)
from tensorflow.keras.models import Model, Sequential, load_model
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping, Callback
from tensorflow.keras.losses import (
    SparseCategoricalCrossentropy, CategoricalCrossentropy, BinaryCrossentropy
)
from tensorflow.keras.utils import to_categorical, Progbar
from tensorflow.keras.optimizers import Adam

# ==============================
# SKLEARN UTILITIES
# ==============================
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler

# Set random seeds for reproducibility
random_state = 42
np.random.seed(random_state)
tf.random.set_seed(random_state)

# Load EMNIST Letters Dataset
emnist_data = pd.read_csv('emnist-letters-train.csv', header=None)
print(f"Dataset loaded successfully!")
print(f"Dataset shape: {emnist_data.shape}")
print(f"Memory usage: {emnist_data.memory_usage(deep=True).sum() / 1e6:.1f} MB")


# Display basic information about the dataset
print("\n Dataset Overview:")
print("="*50)
print(f"Total samples: {len(emnist_data):,}")
print(f"Features per image: {emnist_data.shape[1] - 1}")
print(f"Image dimensions: 28x28 pixels")
print(f"Data type: Grayscale handwritten letters")

# Show first few rows
print("\nFirst 5 rows of the dataset:")
emnist_data.head()

Dataset loaded successfully!
Dataset shape: (64829, 785)
Memory usage: 407.1 MB

 Dataset Overview:
==================================================
Total samples: 64,829
Features per image: 784
Image dimensions: 28x28 pixels
Data type: Grayscale handwritten letters

First 5 rows of the dataset:

# Extract features and labels from the dataset
print("Preprocessing dataset...")

# Separate features (pixel values) and labels (class information)
X_raw = emnist_data.iloc[:, 1:].values  # All columns except first (pixel values)
y_raw = emnist_data.iloc[:, 0].values   # First column (class labels)

# Reshape images to 28x28 format and correct orientation
X_images = X_raw.reshape(-1, 28, 28)
X_images = X_images.transpose(0, 2, 1)  # we will show this later

print(f"Data preprocessing completed!")
print(f"Image array shape: {X_images.shape}")
print(f"Labels array shape: {y_raw.shape}")

# Analyze class distribution
unique_classes, class_counts = np.unique(y_raw, return_counts=True)
print(f"\nClass Distribution Analysis:")
print(f"• Unique classes found: {len(unique_classes)}")
print(f"• Class range: {unique_classes.min()} to {unique_classes.max()}")
print(f"• Total samples: {len(y_raw):,}")

# Create a comprehensive class distribution visualization
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))

# Horizontal bar chart for class distribution
class_df = pd.DataFrame({'Class': unique_classes, 'Count': class_counts})
bars = ax1.barh(class_df['Class'], class_df['Count'], color=plt.cm.viridis(np.linspace(0, 1, len(unique_classes))))

# Add count annotations on bars
for i, (class_num, count) in enumerate(zip(unique_classes, class_counts)):
    ax1.text(count + 50, class_num, f'{count:,}', va='center', fontweight='bold')

ax1.set_xlabel('Number of Samples', fontweight='bold')
ax1.set_ylabel('Class Label', fontweight='bold')
ax1.set_title('Distribution of Classes in EMNIST Letters Dataset', fontweight='bold', fontsize=14)
ax1.grid(axis='x', alpha=0.3)

# Class distribution statistics
ax2.text(0.1, 0.9, f"Dataset Statistics:", fontsize=16, fontweight='bold', transform=ax2.transAxes)
ax2.text(0.1, 0.8, f"• Total Classes: {len(unique_classes)}", fontsize=12, transform=ax2.transAxes)
ax2.text(0.1, 0.7, f"• Samples per class (avg): {class_counts.mean():.0f}", fontsize=12, transform=ax2.transAxes)
ax2.text(0.1, 0.6, f"• Samples per class (std): {class_counts.std():.0f}", fontsize=12, transform=ax2.transAxes)
ax2.text(0.1, 0.5, f"• Most frequent class: {unique_classes[np.argmax(class_counts)]} ({class_counts.max():,} samples)", fontsize=12, transform=ax2.transAxes)
ax2.text(0.1, 0.4, f"• Least frequent class: {unique_classes[np.argmin(class_counts)]} ({class_counts.min():,} samples)", fontsize=12, transform=ax2.transAxes)
ax2.text(0.1, 0.3, f"• Balance ratio: {class_counts.max()/class_counts.min():.2f}:1", fontsize=12, transform=ax2.transAxes)

if -1 in unique_classes:
    empty_class_count = class_counts[unique_classes == -1][0]
    ax2.text(0.1, 0.2, f"Empty class (-1 and -2): {empty_class_count:,} samples", fontsize=12, color='red', transform=ax2.transAxes)

ax2.set_xlim(0, 1)
ax2.set_ylim(0, 1)
ax2.axis('off')

plt.tight_layout()
plt.show()

Preprocessing dataset...
Data preprocessing completed!
Image array shape: (64829, 28, 28)
Labels array shape: (64829,)

Class Distribution Analysis:
• Unique classes found: 18
• Class range: -2 to 26
• Total samples: 64,829

# Define invalid class labels
invalid_classes = [-2, -1]
invalid_mask = np.isin(y_raw, invalid_classes)
invalid_indices = np.where(invalid_mask)[0]

# Show how many are found
print(f"Found {len(invalid_indices):,} samples with invalid labels: {invalid_classes}")

# Display up to 10 invalid samples
n_display = min(10, len(invalid_indices))
if n_display > 0:
    fig, axes = plt.subplots(1, n_display, figsize=(n_display * 2, 2))
    fig.suptitle("Examples of Invalid Class Samples (-1, -2)", fontsize=14, fontweight='bold')
    
    for i in range(n_display):
        idx = invalid_indices[i]
        axes[i].imshow(X_images[idx], cmap='gray')
        axes[i].set_title(f"Label: {y_raw[idx]}", fontsize=9)
        axes[i].axis('off')
    
    plt.tight_layout()
    plt.show()
else:
    print("No samples with invalid class labels found.")

Found 10,240 samples with invalid labels: [-2, -1]

# Load EMNIST Letters Dataset
emnist_data = pd.read_csv('emnist-letters-train.csv', header=None)
print(f"Dataset loaded successfully!")

# Separate features (pixel values) and labels (class information)
X_raw = emnist_data.iloc[:, 1:].values  # All columns except first (pixel values)
y_raw = emnist_data.iloc[:, 0].values   # First column (class labels)

# Reshape images to 28x28 format and correct orientation
X_images = X_raw.reshape(-1, 28, 28)

# Create comprehensive letter mapping and sample visualization
print("Analyzing Letter Classes and Creating Mappings...")

# Remove empty class (-1) if it exists and create proper mappings
valid_classes = [cls for cls in unique_classes if cls != -1]
valid_classes.sort()

# Create letter mapping (EMNIST letters: 1=A, 2=B, ..., 26=Z)
letter_mapping = {i: chr(64 + i) for i in range(1, 27)}  # 1→A, 2→B, etc.
available_letters = [letter_mapping[cls] for cls in valid_classes if cls in letter_mapping]

# Filter data to remove invalid classes (-2, -1)
invalid_classes = [-2, -1]
mask = ~np.isin(y_raw, invalid_classes)
X_filtered = X_images[mask]
y_filtered = y_raw[mask]
removed_count = np.sum(~mask)

# Display sample images for each letter class
print(f"\nSample Images from Each Letter Class:")
available_classes = sorted(np.unique(y_filtered))
samples_per_class = 6

# Calculate grid dimensions
n_classes = len(available_classes)
n_cols = min(13, n_classes)  # Max 13 columns for better display
n_rows_classes = (n_classes + n_cols - 1) // n_cols
total_rows = n_rows_classes * samples_per_class

fig, axes = plt.subplots(total_rows, n_cols, figsize=(n_cols * 1.5, total_rows * 1.5))
fig.suptitle('Sample Images from EMNIST Letters Dataset\n(6 samples per class)', fontsize=16, fontweight='bold')

# Flatten axes for easier indexing
if total_rows == 1:
    axes = axes.reshape(1, -1)
axes_flat = axes.flatten()

# Hide all axes initially
for ax in axes_flat:
    ax.axis('off')

# Plot samples for each class
for class_idx, class_label in enumerate(available_classes):
    # Get samples for this class
    class_samples = X_filtered[y_filtered == class_label]
    
    # Select random samples
    n_samples = min(samples_per_class, len(class_samples))
    sample_indices = np.random.choice(len(class_samples), n_samples, replace=False)
    
    for sample_idx in range(n_samples):
        # Calculate position in grid
        row = (class_idx // n_cols) * samples_per_class + sample_idx
        col = class_idx % n_cols
        
        if row < total_rows and col < n_cols:
            ax_idx = row * n_cols + col
            if ax_idx < len(axes_flat):
                ax = axes_flat[ax_idx]
                
                # Display image
                img = class_samples[sample_indices[sample_idx]]
                ax.imshow(img, cmap='gray', aspect='equal')
                ax.axis('off')
                
                # Add class label on first sample
                if sample_idx == 0:
                    letter = letter_mapping.get(class_label, f'Class {class_label}')
                    ax.set_title(f'{letter} ({class_label})', fontsize=10, fontweight='bold')

plt.tight_layout()
plt.show()

Dataset loaded successfully!
Analyzing Letter Classes and Creating Mappings...

Sample Images from Each Letter Class:

# Load EMNIST Letters Dataset
emnist_data = pd.read_csv('emnist-letters-train.csv', header=None)
print(f"Dataset loaded successfully!")

# Separate features (pixel values) and labels (class information)
X_raw = emnist_data.iloc[:, 1:].values  # All columns except first (pixel values)
y_raw = emnist_data.iloc[:, 0].values   # First column (class labels)

# Reshape images to 28x28 format and correct orientation
X_images = X_raw.reshape(-1, 28, 28)
X_images = X_images.transpose(0, 2, 1)  # we will show this later

# Create comprehensive letter mapping and sample visualization
print("Analyzing Letter Classes and Creating Mappings...")

# Remove empty class (-1) if it exists and create proper mappings
valid_classes = [cls for cls in unique_classes if cls != -1]
valid_classes.sort()

# Create letter mapping (EMNIST letters: 1=A, 2=B, ..., 26=Z)
letter_mapping = {i: chr(64 + i) for i in range(1, 27)}  # 1→A, 2→B, etc.
available_letters = [letter_mapping[cls] for cls in valid_classes if cls in letter_mapping]

print(f"Letter Mapping Summary:")
print(f"• Valid classes: {valid_classes}")
print(f"• Available letters: {available_letters}")
print(f"• Number of letter classes: {len(valid_classes)}")

# Filter data to remove invalid classes (-2, -1)
invalid_classes = [-2, -1]
mask = ~np.isin(y_raw, invalid_classes)
X_filtered = X_images[mask]
y_filtered = y_raw[mask]
removed_count = np.sum(~mask)

# Display sample images for each letter class
print(f"\nSample Images from Each Letter Class:")
available_classes = sorted(np.unique(y_filtered))
samples_per_class = 6

# Calculate grid dimensions
n_classes = len(available_classes)
n_cols = min(13, n_classes)  # Max 13 columns for better display
n_rows_classes = (n_classes + n_cols - 1) // n_cols
total_rows = n_rows_classes * samples_per_class

fig, axes = plt.subplots(total_rows, n_cols, figsize=(n_cols * 1.5, total_rows * 1.5))
fig.suptitle('Sample Images from EMNIST Letters Dataset\n(6 samples per class)', fontsize=16, fontweight='bold')

# Flatten axes for easier indexing
if total_rows == 1:
    axes = axes.reshape(1, -1)
axes_flat = axes.flatten()

# Hide all axes initially
for ax in axes_flat:
    ax.axis('off')

# Plot samples for each class
for class_idx, class_label in enumerate(available_classes):
    # Get samples for this class
    class_samples = X_filtered[y_filtered == class_label]
    
    # Select random samples
    n_samples = min(samples_per_class, len(class_samples))
    sample_indices = np.random.choice(len(class_samples), n_samples, replace=False)
    
    for sample_idx in range(n_samples):
        # Calculate position in grid
        row = (class_idx // n_cols) * samples_per_class + sample_idx
        col = class_idx % n_cols
        
        if row < total_rows and col < n_cols:
            ax_idx = row * n_cols + col
            if ax_idx < len(axes_flat):
                ax = axes_flat[ax_idx]
                
                # Display image
                img = class_samples[sample_indices[sample_idx]]
                ax.imshow(img, cmap='gray', aspect='equal')
                ax.axis('off')
                
                # Add class label on first sample
                if sample_idx == 0:
                    letter = letter_mapping.get(class_label, f'Class {class_label}')
                    ax.set_title(f'{letter} ({class_label})', fontsize=10, fontweight='bold')

plt.tight_layout()
plt.show()

Dataset loaded successfully!
Analyzing Letter Classes and Creating Mappings...
Letter Mapping Summary:
• Valid classes: [-2, 1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 16, 17, 20, 24, 26]
• Available letters: ['A', 'B', 'D', 'E', 'F', 'G', 'I', 'J', 'L', 'N', 'O', 'P', 'Q', 'T', 'X', 'Z']
• Number of letter classes: 17

Sample Images from Each Letter Class:

# Flatten images for statistical analysis
X_flat = X_filtered.reshape(X_filtered.shape[0], -1)
print(f"Flattened data shape: {X_flat.shape}")

# Analyze pixel intensity distribution
plt.figure(figsize=(15, 10))

# Overall pixel intensity distribution
plt.subplot(2, 3, 1)
plt.hist(X_flat.flatten(), bins=50, alpha=0.7, color='skyblue', edgecolor='black')
plt.title('Overall Pixel Intensity Distribution', fontweight='bold')
plt.xlabel('Pixel Intensity')
plt.ylabel('Frequency')
plt.grid(True, alpha=0.3)

# Distribution by class (sample of classes)
plt.subplot(2, 3, 2)
sample_classes = sorted(np.unique(y_filtered))[:8]  # First 8 classes
colors = plt.cm.Set3(np.linspace(0, 1, len(sample_classes)))

for i, class_label in enumerate(sample_classes):
    class_data = X_flat[y_filtered == class_label]
    plt.hist(class_data.flatten(), bins=30, alpha=0.6, 
             label=f'Class {class_label}', color=colors[i], density=True)

plt.title('Pixel Distribution by Class (Sample)', fontweight='bold')
plt.xlabel('Pixel Intensity')
plt.ylabel('Density')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, alpha=0.3)

# Mean intensity per class
plt.subplot(2, 3, 3)
class_means = []
class_labels = []
for class_label in sorted(np.unique(y_filtered)):
    class_data = X_flat[y_filtered == class_label]
    class_means.append(class_data.mean())
    class_labels.append(class_label)

plt.bar(range(len(class_means)), class_means, color='lightcoral', alpha=0.7)
plt.title('Mean Pixel Intensity by Class', fontweight='bold')
plt.xlabel('Class Index')
plt.ylabel('Mean Pixel Intensity')
plt.xticks(range(len(class_labels)), class_labels, rotation=45)
plt.grid(True, alpha=0.3)

# Variance analysis
plt.subplot(2, 3, 4)
class_vars = []
for class_label in sorted(np.unique(y_filtered)):
    class_data = X_flat[y_filtered == class_label]
    class_vars.append(class_data.var())

plt.bar(range(len(class_vars)), class_vars, color='lightgreen', alpha=0.7)
plt.title('Pixel Variance by Class', fontweight='bold')
plt.xlabel('Class Index')
plt.ylabel('Pixel Variance')
plt.xticks(range(len(class_labels)), class_labels, rotation=45)
plt.grid(True, alpha=0.3)

# Sparsity analysis (percentage of zero pixels)
plt.subplot(2, 3, 5)
sparsity_ratios = []
for class_label in sorted(np.unique(y_filtered)):
    class_data = X_flat[y_filtered == class_label]
    zero_ratio = (class_data == 0).sum() / class_data.size
    sparsity_ratios.append(zero_ratio * 100)

plt.bar(range(len(sparsity_ratios)), sparsity_ratios, color='gold', alpha=0.7)
plt.title('Sparsity Ratio by Class (%)', fontweight='bold')
plt.xlabel('Class Index')
plt.ylabel('Zero Pixels (%)')
plt.xticks(range(len(class_labels)), class_labels, rotation=45)
plt.grid(True, alpha=0.3)

# Image complexity analysis (edge detection)
plt.subplot(2, 3, 6)
from scipy import ndimage
edge_counts = []
for class_label in sorted(np.unique(y_filtered)):
    class_images = X_filtered[y_filtered == class_label]
    # Sample 100 images per class for efficiency
    sample_size = min(100, len(class_images))
    sample_indices = np.random.choice(len(class_images), sample_size, replace=False)
    
    total_edges = 0
    for idx in sample_indices:
        # Apply Sobel edge detection
        edges = ndimage.sobel(class_images[idx])
        total_edges += np.sum(edges > edges.mean())
    
    edge_counts.append(total_edges / sample_size)

plt.bar(range(len(edge_counts)), edge_counts, color='mediumpurple', alpha=0.7)
plt.title('Average Edge Count by Class', fontweight='bold')
plt.xlabel('Class Index')
plt.ylabel('Average Edge Pixels')
plt.xticks(range(len(class_labels)), class_labels, rotation=45)
plt.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Flattened data shape: (54589, 784)

# Sample data for correlation analysis (use subset for computational efficiency)
sample_size = 10000
sample_indices = np.random.choice(len(X_flat), min(sample_size, len(X_flat)), replace=False)
X_sample = X_flat[sample_indices]
y_sample = y_filtered[sample_indices]

print(f"Using {len(X_sample):,} samples for correlation analysis")

# Calculate pixel-wise variance to identify most informative features
pixel_variance = np.var(X_sample, axis=0)
pixel_mean = np.mean(X_sample, axis=0)

# Reshape variance and mean to image format for visualization
variance_img = pixel_variance.reshape(28, 28)
mean_img = pixel_mean.reshape(28, 28)

# Create visualization of feature importance
fig, axes = plt.subplots(2, 4, figsize=(16, 8))
fig.suptitle('Feature Importance and Correlation Analysis', fontsize=16, fontweight='bold')

# Pixel variance heatmap
im1 = axes[0, 0].imshow(variance_img, cmap='viridis', aspect='equal')
axes[0, 0].set_title('Pixel Variance Map', fontweight='bold')
axes[0, 0].axis('off')
plt.colorbar(im1, ax=axes[0, 0], shrink=0.8)

# Mean pixel intensity heatmap
im2 = axes[0, 1].imshow(mean_img, cmap='plasma', aspect='equal')
axes[0, 1].set_title('Mean Pixel Intensity Map', fontweight='bold')
axes[0, 1].axis('off')
plt.colorbar(im2, ax=axes[0, 1], shrink=0.8)

# Most discriminative pixels (high variance)
high_var_threshold = np.percentile(pixel_variance, 90)
discriminative_pixels = (pixel_variance > high_var_threshold).reshape(28, 28)
axes[0, 2].imshow(discriminative_pixels, cmap='Reds', aspect='equal')
axes[0, 2].set_title('Most Discriminative Pixels\n(Top 10% Variance)', fontweight='bold')
axes[0, 2].axis('off')

# Least discriminative pixels (low variance)
low_var_threshold = np.percentile(pixel_variance, 10)
non_discriminative_pixels = (pixel_variance < low_var_threshold).reshape(28, 28)
axes[0, 3].imshow(non_discriminative_pixels, cmap='Blues', aspect='equal')
axes[0, 3].set_title('Least Discriminative Pixels\n(Bottom 10% Variance)', fontweight='bold')
axes[0, 3].axis('off')

# Variance distribution
axes[1, 0].hist(pixel_variance, bins=50, alpha=0.7, color='skyblue', edgecolor='black')
axes[1, 0].set_title('Distribution of Pixel Variances', fontweight='bold')
axes[1, 0].set_xlabel('Variance')
axes[1, 0].set_ylabel('Frequency')
axes[1, 0].grid(True, alpha=0.3)

# Mean distribution
axes[1, 1].hist(pixel_mean, bins=50, alpha=0.7, color='lightcoral', edgecolor='black')
axes[1, 1].set_title('Distribution of Pixel Means', fontweight='bold')
axes[1, 1].set_xlabel('Mean Intensity')
axes[1, 1].set_ylabel('Frequency')
axes[1, 1].grid(True, alpha=0.3)

# Correlation between variance and mean
axes[1, 2].scatter(pixel_mean, pixel_variance, alpha=0.6, s=1)
correlation_coef = np.corrcoef(pixel_mean, pixel_variance)[0, 1]
axes[1, 2].set_title(f'Variance vs Mean\n(r = {correlation_coef:.3f})', fontweight='bold')
axes[1, 2].set_xlabel('Mean Intensity')
axes[1, 2].set_ylabel('Variance')
axes[1, 2].grid(True, alpha=0.3)

# Top discriminative pixel positions
top_pixel_indices = np.argsort(pixel_variance)[-20:]  # Top 20 most discriminative
top_positions = [(idx // 28, idx % 28) for idx in top_pixel_indices]

# Create a visualization of top discriminative pixel positions
discriminative_map = np.zeros((28, 28))
for row, col in top_positions:
    discriminative_map[row, col] = 1

axes[1, 3].imshow(discriminative_map, cmap='hot', aspect='equal')
axes[1, 3].set_title('Top 20 Most Discriminative\nPixel Positions', fontweight='bold')
axes[1, 3].axis('off')

plt.tight_layout()
plt.show()


# Identify center vs edge pixel importance
center_region = slice(7, 21)  # Central 14x14 region
edge_region_mask = np.ones((28, 28), dtype=bool)
edge_region_mask[center_region, center_region] = False

Using 10,000 samples for correlation analysis

# Standardize the data before t-SNE
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_sample)

# Use a subset of data for t-SNE (for speed)
tsne_sample_size = 10000
tsne_indices = np.random.choice(len(X_sample), min(tsne_sample_size, len(X_sample)), replace=False)
X_tsne_input = X_scaled[tsne_indices]
y_tsne_input = y_sample[tsne_indices]

print(f"Using {len(X_tsne_input):,} samples for t-SNE analysis")

# Perform t-SNE with different perplexity and learning rate values
perplexity_values = [30, 50, 100]
learning_rates = [200, 500, 1000]
tsne_results = {}

print("\nRunning t-SNE with different hyperparameters...")

# Test different perplexity values
for perp in perplexity_values:
    if perp < len(X_tsne_input):
        tsne = TSNE(n_components=2, perplexity=perp, learning_rate=200, 
                   random_state=42, verbose=0, n_iter=1000)
        X_tsne = tsne.fit_transform(X_tsne_input)
        tsne_results[f'perp_{perp}'] = {
            'embedding': X_tsne,
            'perplexity': perp,
            'learning_rate': 200
        }

# Test different learning rates
optimal_perp = 50 if 50 < len(X_tsne_input) else 30
for lr in learning_rates[1:]:  # Skip 200 (already computed)
    tsne = TSNE(n_components=2, perplexity=optimal_perp, learning_rate=lr, 
               random_state=42, verbose=0, n_iter=1000)
    X_tsne = tsne.fit_transform(X_tsne_input)
    tsne_results[f'lr_{lr}'] = {
        'embedding': X_tsne,
        'perplexity': optimal_perp,
        'learning_rate': lr
    }

# Create t-SNE visualization
fig, axes = plt.subplots(2, 3, figsize=(18, 12))
fig.suptitle('t-SNE Analysis Results', fontsize=16, fontweight='bold')

# Plot perplexity results
for i, (key, result) in enumerate(list(tsne_results.items())[:3]):
    embedding = result['embedding']
    unique_classes_tsne = np.unique(y_tsne_input)
    colors = plt.cm.tab20(np.linspace(0, 1, len(unique_classes_tsne)))
    
    for j, class_label in enumerate(unique_classes_tsne):
        mask = y_tsne_input == class_label
        axes[0, i].scatter(embedding[mask, 0], embedding[mask, 1], 
                           c=[colors[j]], label=f'Class {class_label}', 
                           alpha=0.6, s=15)
    
    title = f"Perplexity = {result['perplexity']}" if 'perp_' in key else f"LR = {result['learning_rate']}"
    axes[0, i].set_title(f't-SNE Embedding\n{title}', fontweight='bold')
    axes[0, i].set_xlabel('t-SNE 1')
    axes[0, i].set_ylabel('t-SNE 2')
    axes[0, i].grid(True, alpha=0.3)

# Add legend to first plot
if len(unique_classes_tsne) <= 15:
    axes[0, 0].legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8)

# Plot learning rate comparisons in bottom row
lr_keys = [key for key in tsne_results.keys() if 'lr_' in key]
for i, key in enumerate(lr_keys[:3]):
    embedding = tsne_results[key]['embedding']
    
    axes[1, i].hexbin(embedding[:, 0], embedding[:, 1], gridsize=30, cmap='Blues', alpha=0.7)
    
    title = f"Learning Rate = {tsne_results[key]['learning_rate']}"
    axes[1, i].set_title(f't-SNE Density Plot\n{title}', fontweight='bold')
    axes[1, i].set_xlabel('t-SNE 1')
    axes[1, i].set_ylabel('t-SNE 2')

plt.tight_layout()
plt.show()

# Cluster Analysis
print("\nt-SNE Cluster Analysis:")

best_key = list(tsne_results.keys())[0]
best_embedding = tsne_results[best_key]['embedding']

# Silhouette score
from sklearn.metrics import silhouette_score
try:
    silhouette_avg = silhouette_score(best_embedding, y_tsne_input)
    print(f"• Silhouette score: {silhouette_avg:.3f}")
except:
    print("• Silhouette score: Could not compute (likely due to single class)")

# Class centers
class_centers = {}
for class_label in np.unique(y_tsne_input):
    mask = y_tsne_input == class_label
    center = np.mean(best_embedding[mask], axis=0)
    class_centers[class_label] = center

# Inter-class distances
if len(class_centers) > 1:
    class_labels_list = list(class_centers.keys())
    distances = [np.linalg.norm(class_centers[a] - class_centers[b]) 
                 for i, a in enumerate(class_labels_list) 
                 for b in class_labels_list[i+1:]]
    
# Intra-class spread
intra_class_spreads = []
for class_label in np.unique(y_tsne_input):
    mask = y_tsne_input == class_label
    points = best_embedding[mask]
    center = class_centers[class_label]
    spread = np.mean(np.linalg.norm(points - center, axis=1))
    intra_class_spreads.append(spread)


# Store results
tsne_embedding = best_embedding
tsne_labels = y_tsne_input

Using 10,000 samples for t-SNE analysis

Running t-SNE with different hyperparameters...

t-SNE Cluster Analysis:
• Silhouette score: -0.048

# Dictionary to store average images for each class
class_averages = {}
class_sample_counts = {}

# Process each class
for class_num in sorted(np.unique(y_train)):
    # Get all images for this class
    class_mask = y_train == class_num
    class_images = X_train[class_mask]
    
    # Calculate average image
    avg_image = np.mean(class_images, axis=0)
    class_averages[class_num] = avg_image
    class_sample_counts[class_num] = len(class_images)
    
    print(f"Class {class_num}: {len(class_images)} samples, average calculated")

# Visualize average images for each class
def visualize_class_averages(class_averages, class_to_letter, title="Average Images by Class"):
    n_classes = len(class_averages)
    cols = min(8, n_classes)
    rows = (n_classes + cols - 1) // cols
    
    fig, axes = plt.subplots(rows, cols, figsize=(cols * 2, rows * 2))
    if rows == 1:
        axes = axes.reshape(1, -1)
    elif cols == 1:
        axes = axes.reshape(-1, 1)
    
    fig.suptitle(title, fontsize=16, fontweight='bold')
    
    for idx, (class_num, avg_image) in enumerate(sorted(class_averages.items())):
        row = idx // cols
        col = idx % cols
        
        ax = axes[row, col]
        ax.imshow(avg_image.squeeze(), cmap='gray')
        
        # Get letter for this class if available
        letter = class_to_letter.get(class_num, f"Class {class_num}")
        sample_count = class_sample_counts[class_num]
        
        ax.set_title(f"{letter}\n({sample_count} samples)", fontsize=10)
        ax.axis('off')
    
    # Hide unused subplots
    total_subplots = rows * cols
    for idx in range(n_classes, total_subplots):
        row = idx // cols
        col = idx % cols
        axes[row, col].axis('off')
    
    plt.tight_layout()
    plt.show()

# Display average images
visualize_class_averages(class_averages, class_to_letter)

# Calculate and display statistics about class averages
print("\nClass Average Image Statistics:")
print("=" * 50)
for class_num in sorted(class_averages.keys()):
    avg_image = class_averages[class_num]
    letter = class_to_letter.get(class_num, f"Class {class_num}")
    
    # Calculate statistics
    mean_intensity = np.mean(avg_image)
    std_intensity = np.std(avg_image)
    min_intensity = np.min(avg_image)
    max_intensity = np.max(avg_image)
    
    print(f"{letter:>2} (Class {class_num:2d}): "
          f"Mean={mean_intensity:.3f}, Std={std_intensity:.3f}, "
          f"Range=[{min_intensity:.3f}, {max_intensity:.3f}]")

# Compare class averages using correlation
print("\nClass Average Correlation Matrix:")
class_nums = sorted(class_averages.keys())
n_classes = len(class_nums)
correlation_matrix = np.zeros((n_classes, n_classes))

for i, class_i in enumerate(class_nums):
    for j, class_j in enumerate(class_nums):
        img_i = class_averages[class_i].flatten()
        img_j = class_averages[class_j].flatten()
        correlation = np.corrcoef(img_i, img_j)[0, 1]
        correlation_matrix[i, j] = correlation

# Visualize correlation matrix
plt.figure(figsize=(10, 8))
im = plt.imshow(correlation_matrix, cmap='coolwarm', vmin=-1, vmax=1)
plt.colorbar(im, label='Correlation Coefficient')
plt.title('Correlation Between Class Average Images', fontsize=14, fontweight='bold')

# Add class labels
class_labels = [class_to_letter.get(class_num, f"C{class_num}") for class_num in class_nums]
plt.xticks(range(n_classes), class_labels, rotation=45)
plt.yticks(range(n_classes), class_labels)

# Add correlation values as text
for i in range(n_classes):
    for j in range(n_classes):
        plt.text(j, i, f'{correlation_matrix[i, j]:.2f}', 
                ha='center', va='center', 
                color='white' if abs(correlation_matrix[i, j]) > 0.5 else 'black')

plt.tight_layout()
plt.show()

Class 0: 2377 samples, average calculated
Class 1: 2377 samples, average calculated
Class 2: 2378 samples, average calculated
Class 3: 2405 samples, average calculated
Class 4: 2376 samples, average calculated
Class 5: 2369 samples, average calculated
Class 6: 2400 samples, average calculated
Class 7: 2382 samples, average calculated
Class 8: 2391 samples, average calculated
Class 9: 2355 samples, average calculated
Class 10: 2386 samples, average calculated
Class 11: 2401 samples, average calculated
Class 12: 2405 samples, average calculated
Class 13: 2405 samples, average calculated
Class 14: 2405 samples, average calculated
Class 15: 2399 samples, average calculated

Class Average Image Statistics:
==================================================
 A (Class  0): Mean=-0.593, Std=0.419, Range=[-1.000, 0.326]
 B (Class  1): Mean=-0.622, Std=0.398, Range=[-1.000, 0.427]
 D (Class  2): Mean=-0.652, Std=0.365, Range=[-1.000, 0.334]
 E (Class  3): Mean=-0.559, Std=0.495, Range=[-1.000, 0.582]
 F (Class  4): Mean=-0.710, Std=0.353, Range=[-1.000, 0.392]
 G (Class  5): Mean=-0.617, Std=0.370, Range=[-1.000, 0.315]
 I (Class  6): Mean=-0.781, Std=0.390, Range=[-1.000, 0.703]
 J (Class  7): Mean=-0.731, Std=0.307, Range=[-1.000, 0.223]
 L (Class  8): Mean=-0.791, Std=0.309, Range=[-1.000, 0.367]
 N (Class  9): Mean=-0.617, Std=0.419, Range=[-1.000, 0.542]
 O (Class 10): Mean=-0.545, Std=0.531, Range=[-1.000, 0.552]
 P (Class 11): Mean=-0.673, Std=0.401, Range=[-1.000, 0.476]
 Q (Class 12): Mean=-0.608, Std=0.376, Range=[-1.000, 0.406]
 T (Class 13): Mean=-0.734, Std=0.315, Range=[-1.000, 0.229]
 X (Class 14): Mean=-0.640, Std=0.451, Range=[-1.000, 0.782]
 Z (Class 15): Mean=-0.608, Std=0.422, Range=[-1.000, 0.420]

Class Average Correlation Matrix:

class AdvancedDataProcessor:
    """
    Advanced data processing pipeline for EMNIST letters dataset
    Handles normalization, label mapping, and data augmentation
    """
    
    def __init__(self, target_classes=None):
        self.target_classes = target_classes
        self.label_mapping = {}
        self.reverse_mapping = {}
        self.class_to_letter = {}
        self.letter_to_class = {}
        self.num_classes = 0
        
    def create_label_mapping(self, labels):
        """Create consistent label mapping from original labels to 0-based indices"""
        if self.target_classes is not None:
            unique_labels = self.target_classes
        else:
            # Filter out invalid classes (-2, -1) and keep only valid letter classes (1-26)
            unique_labels = sorted([label for label in np.unique(labels) if 1 <= label <= 26])
        
        self.num_classes = len(unique_labels)
        
        # Create mappings
        for new_idx, original_label in enumerate(unique_labels):
            self.label_mapping[original_label] = new_idx
            self.reverse_mapping[new_idx] = original_label
            
            # Create letter mappings (1=A, 2=B, etc.)
            if 1 <= original_label <= 26:
                letter = chr(64 + original_label)  # Convert to letter
                self.class_to_letter[new_idx] = letter
                self.letter_to_class[letter] = new_idx
                
        return self.label_mapping
    
    def normalize_images(self, images, method='tanh'):
        """
        Normalize images for GAN training
        method: 'tanh' for [-1,1] range, 'sigmoid' for [0,1] range
        """
        if method == 'tanh':
            # Normalize to [-1, 1] range (recommended for GAN training)
            normalized = (images.astype(np.float32) - 127.5) / 127.5
            print(f"Images normalized to range [{normalized.min():.2f}, {normalized.max():.2f}]")
        elif method == 'sigmoid':
            # Normalize to [0, 1] range
            normalized = images.astype(np.float32) / 255.0
            print(f"Images normalized to range [{normalized.min():.2f}, {normalized.max():.2f}]")
        else:
            raise ValueError("Method must be 'tanh' or 'sigmoid'")
            
        return normalized
    
    def transform_labels(self, labels):
        """Transform original labels to new consecutive indices"""
        # Filter out invalid labels and map to new indices
        valid_mask = np.isin(labels, list(self.label_mapping.keys()))
        valid_labels = labels[valid_mask]
        transformed = np.array([self.label_mapping[label] for label in valid_labels])
        
        return transformed, valid_mask
    
    def get_class_distribution(self, labels):
        """Calculate class distribution for metrics"""
        hist = np.bincount(labels, minlength=self.num_classes)
        return hist / len(labels)
    
    def augment_data(self, images, labels, augmentation_factor=2):
        """
        Apply data augmentation techniques
        """
        print(f"Applying data augmentation (factor: {augmentation_factor})...")
        
        augmented_images = []
        augmented_labels = []
        
        # Keep original data
        augmented_images.append(images)
        augmented_labels.append(labels)
        
        for factor in range(1, augmentation_factor):
            batch_augmented = []
            
            for img in images:
                # Random rotation (-10 to 10 degrees)
                angle = np.random.uniform(-10, 10)
                rotated = self._rotate_image(img, angle)
                
                # Random small translation
                shift_x = np.random.randint(-2, 3)
                shift_y = np.random.randint(-2, 3)
                translated = self._translate_image(rotated, shift_x, shift_y)
                
                # Add slight noise
                noisy = self._add_gaussian_noise(translated, std=0.05)
                
                batch_augmented.append(noisy)
            
            augmented_images.append(np.array(batch_augmented))
            augmented_labels.append(labels.copy())
        
        # Combine all augmented data
        final_images = np.vstack(augmented_images)
        final_labels = np.hstack(augmented_labels)
        
        print(f"Augmentation complete: {len(images):,} → {len(final_images):,} samples")
        return final_images, final_labels
    
    def _rotate_image(self, image, angle):
        """Rotate image by given angle (in degrees)"""
        from scipy.ndimage import rotate
        return rotate(image, angle, reshape=False, mode='constant', cval=0)
    
    def _translate_image(self, image, shift_x, shift_y):
        """Translate image by given pixel amounts"""
        from scipy.ndimage import shift
        return shift(image, [shift_y, shift_x], mode='constant', cval=0)
    
    def _add_gaussian_noise(self, image, std=0.05):
        """Add Gaussian noise to image"""
        noise = np.random.normal(0, std, image.shape)
        return np.clip(image + noise, -1, 1)
    
# Initialize the data processor
data_processor = AdvancedDataProcessor()

# Create label mapping
label_mapping = data_processor.create_label_mapping(y_filtered)

# Transform labels to consecutive indices
y_processed, valid_mask = data_processor.transform_labels(y_filtered)
X_processed = X_filtered[valid_mask]

# Normalize images for GAN training (to [-1, 1] range)
X_normalized = data_processor.normalize_images(X_processed, method='tanh')

# Add channel dimension for CNN compatibility
X_final = X_normalized.reshape(-1, 28, 28, 1)
y_final = y_processed



# Store global variables for easy access
num_classes = data_processor.num_classes
class_to_letter = data_processor.class_to_letter
letter_to_class = data_processor.letter_to_class
label_mapping_dict = data_processor.label_mapping

Images normalized to range [-1.00, 1.00]

def validate_data_quality(X, y, processor):
    """
    Comprehensive data quality validation
    """
    
    # Check for NaN or infinite values
    nan_count = np.isnan(X).sum()
    inf_count = np.isinf(X).sum()
    print(f"• NaN values: {nan_count}")
    print(f"• Infinite values: {inf_count}")
    
    # Check image value ranges
    print(f"• Image value range: [{X.min():.3f}, {X.max():.3f}]")
    print(f"• Image mean: {X.mean():.3f}")
    print(f"• Image std: {X.std():.3f}")
    
    # Check label distribution
    unique_labels, counts = np.unique(y, return_counts=True)
    print(f"• Number of unique labels: {len(unique_labels)}")
    print(f"• Label range: [{unique_labels.min()}, {unique_labels.max()}]")
    
    # Class balance assessment
    min_samples = counts.min()
    max_samples = counts.max()
    balance_ratio = min_samples / max_samples
    print(f"• Samples per class - Min: {min_samples}, Max: {max_samples}")
    print(f"• Class balance ratio: {balance_ratio:.3f}")
    
    if balance_ratio < 0.5:
        print("Warning: Significant class imbalance detected!")
    else:
        print("Classes are reasonably balanced")
    
    # Sample some images to check for artifacts
    sample_indices = np.random.choice(len(X), 5, replace=False)
    print(f"\nSample image statistics:")
    for i, idx in enumerate(sample_indices):
        img = X[idx].squeeze()
        print(f"  Sample {i+1}: mean={img.mean():.3f}, std={img.std():.3f}, "
              f"label={y[idx]} ({processor.class_to_letter.get(y[idx], '?')})")
    
    return {
        'nan_count': nan_count,
        'inf_count': inf_count,
        'value_range': (X.min(), X.max()),
        'balance_ratio': balance_ratio,
        'total_samples': len(X),
        'num_classes': len(unique_labels)
    }

def create_data_splits(X, y, test_size=0.2, validation_size=0.1, random_state=42):
    """
    Create train/validation/test splits with stratification
    """

    # First split: separate test set
    X_temp, X_test, y_temp, y_test = train_test_split(
        X, y, test_size=test_size, random_state=random_state, stratify=y
    )
    
    # Second split: separate validation from training
    val_size_adjusted = validation_size / (1 - test_size)
    X_train, X_val, y_train, y_val = train_test_split(
        X_temp, y_temp, test_size=val_size_adjusted, 
        random_state=random_state, stratify=y_temp
    )
    
    # Verify class distribution in each split
    for split_name, y_split in [("Training", y_train), ("Validation", y_val), ("Test", y_test)]:
        unique, counts = np.unique(y_split, return_counts=True)
        print(f"• {split_name} classes: {len(unique)} unique labels")
    
    return X_train, X_val, X_test, y_train, y_val, y_test

# Perform data quality validation
quality_report = validate_data_quality(X_final, y_final, data_processor)

# Create data splits for training
X_train, X_val, X_test, y_train, y_val, y_test = create_data_splits(
    X_final, y_final, test_size=0.15, validation_size=0.15, random_state=42
)

• NaN values: 0
• Infinite values: 0
• Image value range: [-1.000, 1.000]
• Image mean: -0.655
• Image std: 0.663
• Number of unique labels: 16
• Label range: [0, 15]
• Samples per class - Min: 3365, Max: 3437
• Class balance ratio: 0.979
Classes are reasonably balanced

Sample image statistics:
  Sample 1: mean=-0.574, std=0.692, label=0 (A)
  Sample 2: mean=-0.563, std=0.712, label=9 (N)
  Sample 3: mean=-0.724, std=0.618, label=8 (L)
  Sample 4: mean=-0.591, std=0.730, label=2 (D)
  Sample 5: mean=-0.358, std=0.828, label=5 (G)
• Training classes: 16 unique labels
• Validation classes: 16 unique labels
• Test classes: 16 unique labels

def visualize_processed_data(X_train, y_train, processor, samples_per_class=3):
    """
    Create comprehensive visualization of processed training data
    """
    print("Creating enhanced data visualization...")
    
    # Get unique classes and their counts
    unique_classes = np.unique(y_train)
    class_counts = np.bincount(y_train, minlength=processor.num_classes)
    
    # Calculate grid dimensions
    n_classes = len(unique_classes)
    n_cols = min(6, n_classes)
    n_rows = (n_classes + n_cols - 1) // n_cols
    
    # Create the main visualization
    fig = plt.figure(figsize=(20, 4 * n_rows))
    
    # Add main title
    fig.suptitle('Processed EMNIST Letters Dataset - Sample Overview', 
                 fontsize=20, fontweight='bold', y=0.98)
    
    for idx, class_idx in enumerate(unique_classes):
        # Get samples for this class
        class_mask = y_train == class_idx
        class_images = X_train[class_mask]
        
        # Sample random images from this class
        if len(class_images) >= samples_per_class:
            sample_indices = np.random.choice(len(class_images), samples_per_class, replace=False)
            samples = class_images[sample_indices]
        else:
            samples = class_images
            
        # Create subplot for this class
        ax = plt.subplot(n_rows, n_cols, idx + 1)
        
        # Create a mosaic of samples
        if len(samples) >= 3:
            # Arrange 3 samples in a row
            combined = np.hstack([samples[i].squeeze() for i in range(min(3, len(samples)))])
        else:
            combined = samples[0].squeeze()
            
        # Display the combined image
        ax.imshow(combined, cmap='gray', vmin=-1, vmax=1)
        
        # Get letter representation
        letter = processor.class_to_letter.get(class_idx, '?')
        count = class_counts[class_idx]
        
        # Set title with class info
        ax.set_title(f'Class {class_idx}: "{letter}"\n{count:,} samples', 
                    fontsize=12, fontweight='bold')
        ax.axis('off')
    
    # Remove empty subplots
    for idx in range(n_classes, n_rows * n_cols):
        plt.subplot(n_rows, n_cols, idx + 1)
        plt.axis('off')
    
    plt.tight_layout()
    plt.show()
    
    # Create class distribution bar chart
    plt.figure(figsize=(15, 8))
    
    # Prepare data for plotting
    letters = [processor.class_to_letter.get(i, f'Class_{i}') for i in unique_classes]
    counts = [class_counts[i] for i in unique_classes]
    
    # Create bar plot
    bars = plt.bar(letters, counts, color=plt.cm.Set3(np.linspace(0, 1, len(letters))), 
                   alpha=0.8, edgecolor='black', linewidth=0.8)
    
    # Customize the plot
    plt.title('Class Distribution in Training Set', fontsize=16, fontweight='bold', pad=20)
    plt.xlabel('Letter Classes', fontsize=14, fontweight='bold')
    plt.ylabel('Number of Samples', fontsize=14, fontweight='bold')
    
    # Add value labels on bars
    for bar, count in zip(bars, counts):
        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + max(counts)*0.01,
                f'{count:,}', ha='center', va='bottom', fontweight='bold')
    
    # Add statistics
    mean_samples = np.mean(counts)
    std_samples = np.std(counts)
    plt.axhline(y=mean_samples, color='red', linestyle='--', alpha=0.7, 
                label=f'Mean: {mean_samples:.0f}')
    plt.axhline(y=mean_samples + std_samples, color='orange', linestyle=':', alpha=0.7,
                label=f'Mean + STD: {mean_samples + std_samples:.0f}')
    plt.axhline(y=mean_samples - std_samples, color='orange', linestyle=':', alpha=0.7,
                label=f'Mean - STD: {mean_samples - std_samples:.0f}')
    
    plt.legend(loc='upper right')
    plt.grid(True, alpha=0.3, axis='y')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()

# Visualize the processed data
visualize_processed_data(X_train, y_train, data_processor, samples_per_class=3)

Creating enhanced data visualization...

# =============================================================================
# BASELINE DCGAN - COMPLETE IMPLEMENTATION
# =============================================================================

def build_baseline_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """
    Build baseline DCGAN generator following original DCGAN paper
    Standard architecture with Conv2DTranspose layers
    """
    print(" Building baseline generator...")
    
    # Noise input
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    
    # Label input for conditional generation
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Embed labels
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate noise and label
    x = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Initial dense layer: project to start convolution
    x = tf.keras.layers.Dense(7 * 7 * 256, use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Reshape to start convolution: 7x7x256
    x = tf.keras.layers.Reshape((7, 7, 256))(x)
    
    # First upsampling: 7x7x256 -> 14x14x128
    x = tf.keras.layers.Conv2DTranspose(128, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Second upsampling: 14x14x128 -> 28x28x64
    x = tf.keras.layers.Conv2DTranspose(64, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Final layer: 28x28x64 -> 28x28x1
    x = tf.keras.layers.Conv2DTranspose(1, 5, strides=1, padding='same', activation='tanh')(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='baseline_generator'
    )
    
    return model

def build_baseline_discriminator(img_height=28, img_width=28, num_classes=16):
    """
    Build baseline DCGAN discriminator following original DCGAN paper
    Standard architecture with Conv2D layers
    """
    print(" Building baseline discriminator...")
    
    # Image input
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    
    # Label input
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process image
    x = img_input
    
    # First conv block: 28x28x1 -> 14x14x64
    x = tf.keras.layers.Conv2D(64, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Second conv block: 14x14x64 -> 7x7x128
    x = tf.keras.layers.Conv2D(128, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Third conv block: 7x7x128 -> 4x4x256
    x = tf.keras.layers.Conv2D(256, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Flatten for dense layers
    x = tf.keras.layers.Flatten()(x)
    
    # Process label
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate image features and label
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Dense layers
    x = tf.keras.layers.Dense(1024)(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Output layer (real/fake classification)
    validity = tf.keras.layers.Dense(1, activation='sigmoid', name='validity')(x)
    
    # Auxiliary classifier for label prediction
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='baseline_discriminator'
    )
    
    return model

# Build baseline models
baseline_generator = build_baseline_generator(
    latent_dim=100, 
    num_classes=num_classes, 
    img_height=28, 
    img_width=28
)

baseline_discriminator = build_baseline_discriminator(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes
)

 Building baseline generator...
 Building baseline discriminator...

class GANEvaluationMetrics:
    """
    Comprehensive evaluation metrics for GAN models
    Includes FID, KL divergence, and visualization tools
    """
    
    def __init__(self, data_processor):
        self.data_processor = data_processor
        self.inception_model = None
        self.initialize_inception_model()
        
    def initialize_inception_model(self):
        """Initialize InceptionV3 model for FID calculation"""
        try:
            # Load InceptionV3 without top layers for feature extraction
            self.inception_model = tf.keras.applications.InceptionV3(
                include_top=False,
                pooling='avg',
                input_shape=(299, 299, 3)
            )
            print("InceptionV3 model loaded for FID calculation")
        except Exception as e:
            print(f"Warning: Could not load InceptionV3 model: {e}")
            print("FID calculation will be disabled")
    
    def preprocess_images_for_inception(self, images):
        """Preprocess images for InceptionV3 (resize to 299x299, convert to RGB)"""
        if self.inception_model is None:
            return None
            
        # Convert from [-1, 1] to [0, 255]
        images = (images + 1) * 127.5
        images = tf.cast(images, tf.uint8)
        
        # Resize to 299x299
        images = tf.image.resize(images, [299, 299])
        
        # Convert grayscale to RGB
        images = tf.image.grayscale_to_rgb(images)
        
        # Preprocess for InceptionV3
        images = tf.cast(images, tf.float32)
        images = tf.keras.applications.inception_v3.preprocess_input(images)
        
        return images
    
    def calculate_fid(self, real_images, generated_images, batch_size=50):
        """
        Calculate Frechet Inception Distance (FID)
        Lower FID indicates better quality and diversity
        """
        if self.inception_model is None:
            print("InceptionV3 not available, skipping FID calculation")
            return None
            
        print("Calculating FID score...")
        
        # Preprocess images
        real_processed = self.preprocess_images_for_inception(real_images)
        gen_processed = self.preprocess_images_for_inception(generated_images)
        
        if real_processed is None or gen_processed is None:
            return None
        
        # Get features in batches to avoid memory issues
        def get_features_batched(images, model, batch_size):
            features = []
            for i in range(0, len(images), batch_size):
                batch = images[i:i+batch_size]
                batch_features = model(batch)
                features.append(batch_features)
            return tf.concat(features, axis=0)
        
        # Extract features
        real_features = get_features_batched(real_processed, self.inception_model, batch_size)
        gen_features = get_features_batched(gen_processed, self.inception_model, batch_size)
        
        # Calculate statistics
        mu_real = tf.reduce_mean(real_features, axis=0)
        mu_gen = tf.reduce_mean(gen_features, axis=0)
        
        sigma_real = tfp.stats.covariance(real_features)
        sigma_gen = tfp.stats.covariance(gen_features)
        
        # Calculate FID
        diff = mu_real - mu_gen
        fid_score = tf.reduce_sum(diff**2) + tf.linalg.trace(
            sigma_real + sigma_gen - 2 * tf.linalg.sqrtm(
                tf.matmul(sigma_real, sigma_gen)
            )
        )
        
        return float(fid_score)
    
    def calculate_kl_divergence(self, real_labels, generated_labels):
        """
        Calculate KL divergence between real and generated label distributions
        Lower KL divergence indicates better label distribution matching
        """
        print("Calculating KL divergence...")
        
        # Calculate distributions
        real_dist = self.data_processor.get_class_distribution(real_labels)
        gen_dist = self.data_processor.get_class_distribution(generated_labels)
        
        # Add small epsilon to avoid log(0)
        epsilon = 1e-8
        real_dist = real_dist + epsilon
        gen_dist = gen_dist + epsilon
        
        # Normalize to ensure they sum to 1
        real_dist = real_dist / tf.reduce_sum(real_dist)
        gen_dist = gen_dist / tf.reduce_sum(gen_dist)
        
        # Calculate KL divergence: KL(real || generated)
        kl_div = tf.reduce_sum(real_dist * tf.math.log(real_dist / gen_dist))
        
        return float(kl_div)
    
    def calculate_inception_score(self, generated_images, splits=10):
        """
        Calculate Inception Score (IS)
        Higher IS indicates better quality and diversity
        """
        if self.inception_model is None:
            print("InceptionV3 not available, skipping IS calculation")
            return None, None
            
        print("Calculating Inception Score...")
        
        # Preprocess images
        processed_images = self.preprocess_images_for_inception(generated_images)
        if processed_images is None:
            return None, None
        
        # Get predictions
        preds = self.inception_model(processed_images)
        preds = tf.nn.softmax(preds)
        
        # Calculate IS for each split
        split_size = len(preds) // splits
        scores = []
        
        for i in range(splits):
            start_idx = i * split_size
            if i == splits - 1:
                end_idx = len(preds)
            else:
                end_idx = (i + 1) * split_size
            
            split_preds = preds[start_idx:end_idx]
            
            # Calculate marginal probability
            p_y = tf.reduce_mean(split_preds, axis=0)
            
            # Calculate KL divergence for each image
            kl_divs = []
            for j in range(len(split_preds)):
                kl_div = tf.reduce_sum(split_preds[j] * tf.math.log(
                    split_preds[j] / (p_y + 1e-8) + 1e-8
                ))
                kl_divs.append(kl_div)
            
            # Calculate IS for this split
            is_score = tf.exp(tf.reduce_mean(kl_divs))
            scores.append(float(is_score))
        
        mean_is = np.mean(scores)
        std_is = np.std(scores)
        
        return mean_is, std_is
    
    def plot_training_history(self, history, save_path=None):
        """Plot comprehensive training history"""
        plt.figure(figsize=(20, 12))
        
        epochs = history['epoch']
        
        # Generator and Discriminator Loss
        plt.subplot(2, 3, 1)
        plt.plot(epochs, history['gen_loss'], label='Generator Loss', color='blue', linewidth=2)
        plt.plot(epochs, history['disc_loss'], label='Discriminator Loss', color='red', linewidth=2)
        plt.title('Generator vs Discriminator Loss', fontsize=14, fontweight='bold')
        plt.xlabel('Epoch')
        plt.ylabel('Loss')
        plt.legend()
        plt.grid(True, alpha=0.3)
        
        # Label Classification Accuracy
        plt.subplot(2, 3, 2)
        plt.plot(epochs, history['label_accuracy'], label='Label Accuracy', color='green', linewidth=2)
        plt.title('Label Classification Accuracy', fontsize=14, fontweight='bold')
        plt.xlabel('Epoch')
        plt.ylabel('Accuracy')
        plt.legend()
        plt.grid(True, alpha=0.3)
        
        # FID Score (if available)
        if 'fid_score' in history:
            plt.subplot(2, 3, 3)
            plt.plot(epochs, history['fid_score'], label='FID Score', color='purple', linewidth=2)
            plt.title('FID Score (Lower is Better)', fontsize=14, fontweight='bold')
            plt.xlabel('Epoch')
            plt.ylabel('FID Score')
            plt.legend()
            plt.grid(True, alpha=0.3)
        
        # KL Divergence (if available)
        if 'kl_divergence' in history:
            plt.subplot(2, 3, 4)
            plt.plot(epochs, history['kl_divergence'], label='KL Divergence', color='orange', linewidth=2)
            plt.title('KL Divergence (Lower is Better)', fontsize=14, fontweight='bold')
            plt.xlabel('Epoch')
            plt.ylabel('KL Divergence')
            plt.legend()
            plt.grid(True, alpha=0.3)
        
        # Inception Score (if available)
        if 'inception_score' in history:
            plt.subplot(2, 3, 5)
            plt.plot(epochs, history['inception_score'], label='Inception Score', color='brown', linewidth=2)
            plt.title('Inception Score (Higher is Better)', fontsize=14, fontweight='bold')
            plt.xlabel('Epoch')
            plt.ylabel('IS')
            plt.legend()
            plt.grid(True, alpha=0.3)
        
        # Loss Difference
        plt.subplot(2, 3, 6)
        loss_diff = np.array(history['gen_loss']) - np.array(history['disc_loss'])
        plt.plot(epochs, loss_diff, label='Gen Loss - Disc Loss', color='magenta', linewidth=2)
        plt.axhline(y=0, color='black', linestyle='--', alpha=0.5)
        plt.title('Loss Difference (Generator - Discriminator)', fontsize=14, fontweight='bold')
        plt.xlabel('Epoch')
        plt.ylabel('Loss Difference')
        plt.legend()
        plt.grid(True, alpha=0.3)
        
        plt.tight_layout()
        
        if save_path:
            plt.savefig(save_path, dpi=300, bbox_inches='tight')
            print(f"Training history saved to {save_path}")
        
        plt.show()
    
    def visualize_generated_samples(self, generator, num_samples_per_class=3, class_subset=None):
        """Visualize generated samples for each class"""
        if class_subset is None:
            class_subset = list(range(self.data_processor.num_classes))
        
        print(f"Generating samples for visualization...")
        
        # Generate samples
        all_images = []
        all_labels = []
        
        for class_idx in class_subset:
            noise = tf.random.normal([num_samples_per_class, 100])
            labels = tf.fill([num_samples_per_class], class_idx)
            
            generated = generator([noise, labels], training=False)
            all_images.append(generated)
            all_labels.extend([class_idx] * num_samples_per_class)
        
        generated_images = tf.concat(all_images, axis=0)
        
        # Create visualization
        n_classes = len(class_subset)
        n_cols = min(8, n_classes)
        n_rows = (n_classes + n_cols - 1) // n_cols
        
        fig = plt.figure(figsize=(n_cols * 2, n_rows * 2 * num_samples_per_class))
        fig.suptitle('Generated Letter Samples by Class', fontsize=16, fontweight='bold')
        
        for idx, class_idx in enumerate(class_subset):
            for sample_idx in range(num_samples_per_class):
                img_idx = idx * num_samples_per_class + sample_idx
                
                row = (idx // n_cols) * num_samples_per_class + sample_idx
                col = idx % n_cols
                
                ax = plt.subplot(n_rows * num_samples_per_class, n_cols, row * n_cols + col + 1)
                
                # Display image
                img = generated_images[img_idx].numpy().squeeze()
                ax.imshow(img, cmap='gray', vmin=-1, vmax=1)
                ax.axis('off')
                
                # Add class label on first sample
                if sample_idx == 0:
                    letter = self.data_processor.class_to_letter.get(class_idx, f'Class_{class_idx}')
                    ax.set_title(f'{letter} (Class {class_idx})', fontsize=10, fontweight='bold')
        
        plt.tight_layout()
        plt.show()
        
        return generated_images, np.array(all_labels)

# Try to import tensorflow_probability for advanced statistics
try:
    import tensorflow_probability as tfp
    print("TensorFlow Probability available for advanced metrics")
except ImportError:
    print("TensorFlow Probability not available - some metrics may be limited")
    # Create a minimal replacement
    class tfp:
        class stats:
            @staticmethod
            def covariance(x):
                mean_x = tf.reduce_mean(x, axis=0, keepdims=True)
                x_centered = x - mean_x
                return tf.matmul(x_centered, x_centered, transpose_a=True) / (tf.cast(tf.shape(x)[0], tf.float32) - 1)

# Initialize evaluation metrics
evaluation_metrics = GANEvaluationMetrics(data_processor)
print("Evaluation metrics ready!")

TensorFlow Probability not available - some metrics may be limited
InceptionV3 model loaded for FID calculation
Evaluation metrics ready!

# =============================================================================
# BASELINE DCGAN TRAINER AND VISUALIZATION
# =============================================================================
# Define training parameters
BATCH_SIZE = 64
BUFFER_SIZE = 1000

class BaselineGANTrainer:
    """
    Baseline GAN training pipeline following standard DCGAN training procedure
    """
    
    def __init__(self, generator, discriminator, latent_dim=100, num_classes=16):
        self.generator = generator
        self.discriminator = discriminator
        self.latent_dim = latent_dim
        self.num_classes = num_classes
        
        # Standard DCGAN optimizers (as per original paper)
        self.gen_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
        self.disc_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
        
        # Loss functions
        self.bce_loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)
        self.categorical_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
        
        # Training history
        self.history = {
            'gen_loss': [],
            'disc_loss': [],
            'label_accuracy': [],
            'epoch': []
        }
    
    @tf.function
    def train_discriminator_step(self, real_images, real_labels, batch_size):
        """Single discriminator training step"""
        # Generate fake images
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], 0, self.num_classes, dtype=tf.int32)
        fake_images = self.generator([noise, fake_labels], training=True)
        
        with tf.GradientTape() as tape:
            # Real images
            real_validity, real_label_pred = self.discriminator([real_images, real_labels], training=True)
            
            # Fake images
            fake_validity, fake_label_pred = self.discriminator([fake_images, fake_labels], training=True)
            
            # Adversarial losses
            real_loss = self.bce_loss(tf.ones_like(real_validity), real_validity)
            fake_loss = self.bce_loss(tf.zeros_like(fake_validity), fake_validity)
            
            # Label classification losses
            real_label_loss = self.categorical_loss(real_labels, real_label_pred)
            fake_label_loss = self.categorical_loss(fake_labels, fake_label_pred)
            
            # Total discriminator loss
            disc_loss = (real_loss + fake_loss) / 2 + (real_label_loss + fake_label_loss) / 2
        
        # Update discriminator
        gradients = tape.gradient(disc_loss, self.discriminator.trainable_variables)
        self.disc_optimizer.apply_gradients(zip(gradients, self.discriminator.trainable_variables))
        
        return disc_loss, real_label_pred, real_labels
    
    @tf.function
    def train_generator_step(self, batch_size):
        """Single generator training step"""
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], 0, self.num_classes, dtype=tf.int32)
        
        with tf.GradientTape() as tape:
            fake_images = self.generator([noise, fake_labels], training=True)
            fake_validity, fake_label_pred = self.discriminator([fake_images, fake_labels], training=True)
            
            # Generator wants discriminator to classify fake images as real
            adversarial_loss = self.bce_loss(tf.ones_like(fake_validity), fake_validity)
            
            # Generator wants correct label classification
            label_loss = self.categorical_loss(fake_labels, fake_label_pred)
            
            # Total generator loss
            gen_loss = adversarial_loss + label_loss
        
        # Update generator
        gradients = tape.gradient(gen_loss, self.generator.trainable_variables)
        self.gen_optimizer.apply_gradients(zip(gradients, self.generator.trainable_variables))
        
        return gen_loss
    
    def train_epoch(self, dataset, epoch, steps_per_epoch):
        """Train for one epoch"""
        gen_loss_avg = tf.keras.metrics.Mean()
        disc_loss_avg = tf.keras.metrics.Mean()
        accuracy_avg = tf.keras.metrics.SparseCategoricalAccuracy()
        
        # Training progress bar
        progress_bar = tqdm(enumerate(dataset.take(steps_per_epoch)), 
                           total=steps_per_epoch, 
                           desc=f"Baseline Epoch {epoch + 1}")
        
        for step, (real_images, real_labels) in progress_bar:
            batch_size = tf.shape(real_images)[0]
            
            # Train discriminator
            disc_loss, real_pred, real_labels_batch = self.train_discriminator_step(
                real_images, real_labels, batch_size
            )
            
            # Train generator
            gen_loss = self.train_generator_step(batch_size)
            
            # Update metrics
            gen_loss_avg.update_state(gen_loss)
            disc_loss_avg.update_state(disc_loss)
            accuracy_avg.update_state(real_labels_batch, real_pred)
            
            # Update progress bar
            if step % 20 == 0:
                progress_bar.set_postfix({
                    'Gen Loss': f'{gen_loss_avg.result():.4f}',
                    'Disc Loss': f'{disc_loss_avg.result():.4f}',
                    'Accuracy': f'{accuracy_avg.result():.4f}'
                })
        
        # Store epoch results
        final_gen_loss = float(gen_loss_avg.result())
        final_disc_loss = float(disc_loss_avg.result())
        final_accuracy = float(accuracy_avg.result())
        
        self.history['gen_loss'].append(final_gen_loss)
        self.history['disc_loss'].append(final_disc_loss)
        self.history['label_accuracy'].append(final_accuracy)
        self.history['epoch'].append(epoch)
        
        print(f"Epoch {epoch + 1} - Gen Loss: {final_gen_loss:.4f}, Disc Loss: {final_disc_loss:.4f}, Accuracy: {final_accuracy:.4f}")

def display_generated_samples_grid_baseline(generator, class_to_letter, epoch=None, samples_per_class=6):
    """
    Display generated samples for all classes in a grid format
    Same visualization as enhanced version for comparison
    """
    print(f"Generating {samples_per_class} samples per class for all {len(class_to_letter)} letter classes...")
    
    # Generate samples for each class
    all_images = []
    all_labels = []
    
    for class_num in sorted(class_to_letter.keys()):
        # Generate noise and labels for this class
        noise = tf.random.normal([samples_per_class, 100])  # latent_dim = 100
        labels = tf.fill([samples_per_class], class_num)
        
        # Generate images
        generated_imgs = generator([noise, labels], training=False)
        
        all_images.append(generated_imgs)
        all_labels.extend([class_num] * samples_per_class)
    
    # Concatenate all generated images
    all_images = tf.concat(all_images, axis=0)
    
    # Convert to numpy for plotting
    generated_images = (all_images.numpy() + 1) / 2.0  # Convert from [-1,1] to [0,1]
    
    # Create the grid plot
    n_classes = len(class_to_letter)
    n_samples = samples_per_class
    
    # Create figure
    fig, axes = plt.subplots(n_samples, n_classes, figsize=(n_classes * 1.2, n_samples * 1.2))
    
    # Set title
    title = f"Generated Letter Samples - All Classes"
    if epoch is not None:
        title += f" (Epoch {epoch})"
    title += f"\\n{samples_per_class} samples per class"
    fig.suptitle(title, fontsize=14, fontweight='bold')
    
    # Plot images
    for class_idx, (class_num, letter) in enumerate(sorted(class_to_letter.items())):
        # Add column header
        axes[0, class_idx].set_title(f"{letter} (Class {class_num})", fontsize=10, fontweight='bold')
        
        # Plot samples for this class
        for sample_idx in range(samples_per_class):
            img_idx = class_idx * samples_per_class + sample_idx
            
            ax = axes[sample_idx, class_idx]
            ax.imshow(generated_images[img_idx, :, :, 0], cmap='gray')
            ax.axis('off')
    
    plt.tight_layout()
    plt.show()
    
    return all_images, all_labels

# Initialize baseline trainer
print("Initializing Baseline GAN Trainer...")
baseline_trainer = BaselineGANTrainer(
    generator=baseline_generator,
    discriminator=baseline_discriminator,
    latent_dim=100,
    num_classes=num_classes
)

# Create TensorFlow dataset for efficient training
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=True)
train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)

# Calculate steps per epoch
steps_per_epoch = len(X_train) // BATCH_SIZE

Initializing Baseline GAN Trainer...

# =============================================================================
# BASELINE DCGAN TRAINING - COMPLETE 50 EPOCH TRAINING
# =============================================================================

# Test visualization first
print("Testing visualization with current baseline models...")
test_images, test_labels = display_generated_samples_grid_baseline(
    baseline_generator, class_to_letter, samples_per_class=6
)

# Training configuration
NUM_EPOCHS = 50

start_time = time.time()

for epoch in range(NUM_EPOCHS):
    epoch_start = time.time()
    
    # Train for one epoch
    baseline_trainer.train_epoch(train_dataset, epoch, steps_per_epoch)
    
    # Display only on the last epoch
    if (epoch + 1) == NUM_EPOCHS:
        print(f"\nGenerating Grid of Samples at Final Epoch {epoch + 1}:")
        display_generated_samples_grid_baseline(baseline_generator, class_to_letter, epoch + 1, samples_per_class=6)
    
    # Calculate and display epoch timing
    epoch_time = time.time() - epoch_start
    total_time = time.time() - start_time
    avg_time = total_time / (epoch + 1)
    eta = avg_time * (NUM_EPOCHS - epoch - 1)
    
total_training_time = time.time() - start_time


# Generate final display
print(f"\nFinal Generated Samples - All Letter Classes:")
final_images, final_labels = display_generated_samples_grid_baseline(
    baseline_generator, class_to_letter, NUM_EPOCHS, samples_per_class=6
)

# Display comprehensive training summary
available_letters = sorted(class_to_letter.values())
missing_letters = [letter for letter in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' if letter not in available_letters]

# Plot training progress
if len(baseline_trainer.history['gen_loss']) > 1:
    plt.figure(figsize=(15, 5))
    
    # Generator and Discriminator Loss
    plt.subplot(1, 3, 1)
    epochs = baseline_trainer.history['epoch']
    plt.plot(epochs, baseline_trainer.history['gen_loss'], label='Generator Loss', color='blue', linewidth=2)
    plt.plot(epochs, baseline_trainer.history['disc_loss'], label='Discriminator Loss', color='red', linewidth=2)
    plt.title('Baseline DCGAN - Training Losses', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Discriminator Accuracy
    plt.subplot(1, 3, 2)
    plt.plot(epochs, baseline_trainer.history['label_accuracy'], label='Discriminator Accuracy', color='green', linewidth=2)
    plt.axhline(y=0.95, color='red', linestyle='--', alpha=0.7, label='Upper limit (95%)')
    plt.axhline(y=0.70, color='orange', linestyle='--', alpha=0.7, label='Lower limit (70%)')
    plt.title('Baseline DCGAN - Discriminator Accuracy', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Loss Difference
    plt.subplot(1, 3, 3)
    loss_diff = [g - d for g, d in zip(baseline_trainer.history['gen_loss'], baseline_trainer.history['disc_loss'])]
    plt.plot(epochs, loss_diff, label='Gen Loss - Disc Loss', color='purple', linewidth=2)
    plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)
    plt.title('Baseline DCGAN - Loss Balance', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss Difference')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()

Testing visualization with current baseline models...
Generating 6 samples per class for all 16 letter classes...

Baseline Epoch 1: 100%|██████████| 597/597 [00:58<00:00, 10.12it/s, Gen Loss=2.4564, Disc Loss=1.4497, Accuracy=0.7198]

Epoch 1 - Gen Loss: 2.4375, Disc Loss: 1.4306, Accuracy: 0.7254

Baseline Epoch 2: 100%|██████████| 597/597 [00:48<00:00, 12.25it/s, Gen Loss=1.4203, Disc Loss=0.6453, Accuracy=0.9699]

Epoch 2 - Gen Loss: 1.4160, Disc Loss: 0.6447, Accuracy: 0.9705

Baseline Epoch 3: 100%|██████████| 597/597 [00:51<00:00, 11.59it/s, Gen Loss=1.4047, Disc Loss=0.5791, Accuracy=0.9959]

Epoch 3 - Gen Loss: 1.4021, Disc Loss: 0.5786, Accuracy: 0.9959

Baseline Epoch 4: 100%|██████████| 597/597 [00:49<00:00, 12.06it/s, Gen Loss=1.3079, Disc Loss=0.5819, Accuracy=0.9990]

Epoch 4 - Gen Loss: 1.3076, Disc Loss: 0.5812, Accuracy: 0.9990

Baseline Epoch 5: 100%|██████████| 597/597 [00:46<00:00, 12.77it/s, Gen Loss=1.2661, Disc Loss=0.5754, Accuracy=0.9994]

# =============================================================================
# ENHANCED DCGAN 
# =============================================================================

# -----------------------------------------------------------------------------
# ENHANCED GENERATOR COMPONENTS
# -----------------------------------------------------------------------------

class ResidualBlock(tf.keras.layers.Layer):
    """Residual block for enhanced generator - improves gradient flow"""
    def __init__(self, filters, kernel_size=3, strides=1, **kwargs):
        super(ResidualBlock, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = kernel_size
        self.strides = strides
        
        # Main path
        self.conv1 = tf.keras.layers.Conv2DTranspose(
            filters, kernel_size, strides=strides, padding='same', use_bias=False
        )
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.relu1 = tf.keras.layers.ReLU()
        
        self.conv2 = tf.keras.layers.Conv2DTranspose(
            filters, kernel_size, strides=1, padding='same', use_bias=False
        )
        self.bn2 = tf.keras.layers.BatchNormalization()
        
        # Skip connection (if needed)
        self.skip_conv = None
        if strides != 1:
            self.skip_conv = tf.keras.layers.Conv2DTranspose(
                filters, 1, strides=strides, padding='same', use_bias=False
            )
            self.skip_bn = tf.keras.layers.BatchNormalization()
        
        self.final_relu = tf.keras.layers.ReLU()
    
    def call(self, inputs, training=None):
        # Main path
        x = self.conv1(inputs, training=training)
        x = self.bn1(x, training=training)
        x = self.relu1(x)
        
        x = self.conv2(x, training=training)
        x = self.bn2(x, training=training)
        
        # Skip connection
        if self.skip_conv is not None:
            skip = self.skip_conv(inputs, training=training)
            skip = self.skip_bn(skip, training=training)
        else:
            skip = inputs
        
        # Add skip connection and apply final activation
        x = x + skip
        return self.final_relu(x)

class SelfAttention(tf.keras.layers.Layer):
    """Self-attention mechanism for better feature learning"""
    def __init__(self, **kwargs):
        super(SelfAttention, self).__init__(**kwargs)
    
    def build(self, input_shape):
        self.channels = input_shape[-1]
        
        # Query, Key, Value convolutions
        self.query_conv = tf.keras.layers.Conv2D(self.channels // 8, 1, use_bias=False)
        self.key_conv = tf.keras.layers.Conv2D(self.channels // 8, 1, use_bias=False)
        self.value_conv = tf.keras.layers.Conv2D(self.channels, 1, use_bias=False)
        
        # Output convolution
        self.out_conv = tf.keras.layers.Conv2D(self.channels, 1, use_bias=False)
        
        # Learnable parameter for attention weight
        self.gamma = self.add_weight(name='gamma', shape=(), initializer='zeros', trainable=True)
    
    def call(self, inputs):
        batch_size, height, width, channels = tf.shape(inputs)[0], tf.shape(inputs)[1], tf.shape(inputs)[2], tf.shape(inputs)[3]
        
        # Generate query, key, value
        query = self.query_conv(inputs)
        key = self.key_conv(inputs)
        value = self.value_conv(inputs)
        
        # Reshape for matrix multiplication
        query = tf.reshape(query, [batch_size, height * width, channels // 8])
        key = tf.reshape(key, [batch_size, height * width, channels // 8])
        value = tf.reshape(value, [batch_size, height * width, channels])
        
        # Compute attention
        attention = tf.nn.softmax(tf.matmul(query, key, transpose_b=True))
        out = tf.matmul(attention, value)
        
        # Reshape back
        out = tf.reshape(out, [batch_size, height, width, channels])
        out = self.out_conv(out)
        
        # Apply attention with learnable weight
        return inputs + self.gamma * out

def build_enhanced_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """Build enhanced generator with residual blocks and self-attention"""
    
    # Noise input
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    
    # Label input for conditional generation
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Embed labels
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate noise and label
    x = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Initial dense layer
    x = tf.keras.layers.Dense(7 * 7 * 256, use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Reshape to start convolution
    x = tf.keras.layers.Reshape((7, 7, 256))(x)
    
    # First residual block (7x7x256 -> 14x14x128)
    x = ResidualBlock(128, strides=2, name='res_block_1')(x)
    
    # Add self-attention at intermediate resolution
    x = SelfAttention(name='self_attention')(x)
    
    # Second residual block (14x14x128 -> 28x28x64)
    x = ResidualBlock(64, strides=2, name='res_block_2')(x)
    
    # Final convolution to get single channel
    x = tf.keras.layers.Conv2DTranspose(
        1, 7, strides=1, padding='same', activation='tanh', name='output_conv'
    )(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='enhanced_generator'
    )
    
    return model

# -----------------------------------------------------------------------------
# ENHANCED DISCRIMINATOR COMPONENTS
# -----------------------------------------------------------------------------

class SpectralNormalization(tf.keras.layers.Wrapper):
    """Spectral Normalization layer for improved training stability"""
    def __init__(self, layer, **kwargs):
        super(SpectralNormalization, self).__init__(layer, **kwargs)
        self.power_iterations = 1
    
    def build(self, input_shape):
        super(SpectralNormalization, self).build(input_shape)
        
        # Get weight matrix
        if hasattr(self.layer, 'kernel'):
            self.w = self.layer.kernel
        else:
            raise ValueError('Layer does not have kernel weights')
        
        # Initialize u vector for power iteration
        w_shape = self.w.shape.as_list()
        self.u = self.add_weight(
            shape=(1, w_shape[-1]),
            initializer='random_normal',
            name='u',
            trainable=False
        )
    
    def call(self, inputs, training=None):
        # Perform spectral normalization
        w_reshaped = tf.reshape(self.w, [-1, self.w.shape[-1]])
        
        # Power iteration
        u = self.u
        for _ in range(self.power_iterations):
            v = tf.nn.l2_normalize(tf.matmul(u, w_reshaped, transpose_b=True))
            u = tf.nn.l2_normalize(tf.matmul(v, w_reshaped))
        
        # Update u
        self.u.assign(u)
        
        # Compute spectral norm
        sigma = tf.matmul(tf.matmul(v, w_reshaped), u, transpose_b=True)
        
        # Normalize weights
        w_normalized = self.w / sigma
        
        # Temporarily replace weights
        original_w = self.layer.kernel
        self.layer.kernel = w_normalized
        
        # Call the layer
        output = self.layer(inputs, training=training)
        
        # Restore original weights
        self.layer.kernel = original_w
        
        return output

def build_enhanced_discriminator(img_height=28, img_width=28, num_classes=16):
    """Build enhanced discriminator with spectral normalization"""
    
    # Image input
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    
    # Label input
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process image
    x = img_input
    
    # First conv block (28x28x1 -> 14x14x64)
    x = SpectralNormalization(
        tf.keras.layers.Conv2D(64, 4, strides=2, padding='same')
    )(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Second conv block (14x14x64 -> 7x7x128)
    x = SpectralNormalization(
        tf.keras.layers.Conv2D(128, 4, strides=2, padding='same')
    )(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Third conv block (7x7x128 -> 4x4x256)
    x = SpectralNormalization(
        tf.keras.layers.Conv2D(256, 4, strides=2, padding='same')
    )(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Flatten for dense layers
    x = tf.keras.layers.Flatten()(x)
    
    # Process label
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate image features and label
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Dense layers
    x = tf.keras.layers.Dense(512)(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Output layers
    validity = tf.keras.layers.Dense(1, activation='sigmoid', name='validity')(x)
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='enhanced_discriminator'
    )
    
    return model

# -----------------------------------------------------------------------------
# 3. BUILD ENHANCED MODELS
# -----------------------------------------------------------------------------

# Build enhanced models with correct number of classes
enhanced_generator = build_enhanced_generator(
    latent_dim=100, 
    num_classes=num_classes,  # 16 classes
    img_height=28, 
    img_width=28
)

enhanced_discriminator = build_enhanced_discriminator(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes  # 16 classes
)

# =============================================================================
# BALANCED GAN TRAINER & VISUALIZATION
# =============================================================================

class BalancedGANTrainer:
    """Balanced GAN training pipeline that prevents discriminator dominance"""
    
    def __init__(self, generator, discriminator, latent_dim=100, num_classes=16):
        self.generator = generator
        self.discriminator = discriminator
        self.latent_dim = latent_dim
        self.num_classes = num_classes
        
        # Balanced learning rates: slower discriminator, faster generator
        self.gen_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
        self.disc_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001, beta_1=0.5)  # Slower
        
        # Loss functions
        self.bce_loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)
        self.categorical_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
        
        # Label smoothing parameters
        self.real_label_smoothing = 0.9  # Instead of 1.0
        self.fake_label_smoothing = 0.1  # Instead of 0.0
        
        # Noise injection parameters
        self.noise_std = 0.05  # Add noise to discriminator inputs
        
        # Training frequency (train generator more often)
        self.gen_train_freq = 2  # Train generator 2 times per discriminator training
        
        # Metrics for tracking
        self.gen_loss_metric = tf.keras.metrics.Mean(name='gen_loss')
        self.disc_loss_metric = tf.keras.metrics.Mean(name='disc_loss')
        self.acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(name='label_accuracy')
        
        # Training history
        self.history = {
            'gen_loss': [],
            'disc_loss': [],
            'label_accuracy': [],
            'epoch': []
        }
    
    def add_noise_to_images(self, images):
        """Add noise to images to make discriminator task harder"""
        noise = tf.random.normal(tf.shape(images), stddev=self.noise_std)
        return tf.clip_by_value(images + noise, -1.0, 1.0)
    
    @tf.function
    def train_discriminator(self, real_images, real_labels, batch_size):
        """Train discriminator with label smoothing and noise injection"""
        # Generate fake images
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], 0, self.num_classes, dtype=tf.int32)
        fake_images = self.generator([noise, fake_labels], training=True)
        
        # Add noise to both real and fake images
        real_images_noisy = self.add_noise_to_images(real_images)
        fake_images_noisy = self.add_noise_to_images(fake_images)
        
        with tf.GradientTape() as tape:
            # Real images with label smoothing
            real_validity, real_label_pred = self.discriminator([real_images_noisy, real_labels], training=True)
            
            # Fake images with label smoothing
            fake_validity, fake_label_pred = self.discriminator([fake_images_noisy, fake_labels], training=True)
            
            # Adversarial losses with label smoothing
            real_loss = self.bce_loss(
                tf.ones_like(real_validity) * self.real_label_smoothing, 
                real_validity
            )
            fake_loss = self.bce_loss(
                tf.ones_like(fake_validity) * self.fake_label_smoothing, 
                fake_validity
            )
            
            # Label classification losses
            real_label_loss = self.categorical_loss(real_labels, real_label_pred)
            fake_label_loss = self.categorical_loss(fake_labels, fake_label_pred)
            
            # Total discriminator loss
            disc_loss = (real_loss + fake_loss) / 2 + (real_label_loss + fake_label_loss) / 2
        
        # Update discriminator
        gradients = tape.gradient(disc_loss, self.discriminator.trainable_variables)
        self.disc_optimizer.apply_gradients(zip(gradients, self.discriminator.trainable_variables))
        
        # Update metrics
        self.disc_loss_metric.update_state(disc_loss)
        self.acc_metric.update_state(real_labels, real_label_pred)
        
        return disc_loss
    
    @tf.function
    def train_generator(self, batch_size):
        """Train generator with stronger loss to compete with discriminator"""
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], 0, self.num_classes, dtype=tf.int32)
        
        with tf.GradientTape() as tape:
            fake_images = self.generator([noise, fake_labels], training=True)
            fake_validity, fake_label_pred = self.discriminator([fake_images, fake_labels], training=True)
            
            # Generator wants discriminator to classify fake images as real (no smoothing here)
            adversarial_loss = self.bce_loss(tf.ones_like(fake_validity), fake_validity)
            
            # Generator wants correct label classification
            label_loss = self.categorical_loss(fake_labels, fake_label_pred)
            
            # Total generator loss with stronger adversarial component
            gen_loss = adversarial_loss * 1.5 + label_loss  # Boost adversarial loss
        
        # Update generator
        gradients = tape.gradient(gen_loss, self.generator.trainable_variables)
        self.gen_optimizer.apply_gradients(zip(gradients, self.generator.trainable_variables))
        
        # Update metrics
        self.gen_loss_metric.update_state(gen_loss)
        
        return gen_loss
    
    def train_epoch(self, dataset, epoch, steps_per_epoch):
        """Train for one epoch with balanced training strategy"""
        print(f"\nEpoch {epoch + 1}")
        print("-" * 60)
        
        # Reset metrics
        self.gen_loss_metric.reset_states()
        self.disc_loss_metric.reset_states()
        self.acc_metric.reset_states()
        
        # Training loop with balanced strategy
        for step, (real_images, real_labels) in enumerate(dataset.take(steps_per_epoch)):
            batch_size = tf.shape(real_images)[0]
            
            # Train discriminator less frequently to prevent it from being too strong
            if step % 2 == 0:  # Train discriminator every other step
                disc_loss = self.train_discriminator(real_images, real_labels, batch_size)
            
            # Train generator more frequently
            for _ in range(self.gen_train_freq):
                gen_loss = self.train_generator(batch_size)
            
            # Print progress
            if step % 100 == 0:
                print(f"Step {step:4d}/{steps_per_epoch} - "
                      f"Gen Loss: {self.gen_loss_metric.result():.4f}, "
                      f"Disc Loss: {self.disc_loss_metric.result():.4f}, "
                      f"Accuracy: {self.acc_metric.result():.4f}")
        
        # Store epoch results
        self.history['gen_loss'].append(float(self.gen_loss_metric.result()))
        self.history['disc_loss'].append(float(self.disc_loss_metric.result()))
        self.history['label_accuracy'].append(float(self.acc_metric.result()))
        self.history['epoch'].append(epoch)

        # Check if discriminator is still too strong
        acc = self.acc_metric.result()
        if acc > 0.95:
            print("Discriminator accuracy > 95% - consider further balancing")
        elif acc < 0.70:
            print("Discriminator accuracy < 70% - discriminator might be too weak")
        else:
            print("Discriminator accuracy in good range (70-95%)")

def display_generated_samples_grid(generator, class_to_letter, epoch=None, samples_per_class=6):
    """Generate and display multiple samples per class in grid format"""
    print(f"Generating {samples_per_class} samples per class for all {len(class_to_letter)} letter classes...")
    
    # Generate samples for each class
    all_images = []
    all_labels = []
    
    for class_idx in sorted(class_to_letter.keys()):
        # Generate multiple samples for this class
        noise = tf.random.normal([samples_per_class, 100])
        labels = tf.fill([samples_per_class], class_idx)
        generated_images = generator([noise, labels], training=False)
        
        all_images.append(generated_images)
        all_labels.extend([class_idx] * samples_per_class)
    
    # Concatenate all generated images
    all_generated = tf.concat(all_images, axis=0)
    
    # Calculate grid dimensions
    n_classes = len(class_to_letter)
    n_cols = min(13, n_classes)  # Max 13 columns to fit screen
    n_rows_per_class = samples_per_class
    total_rows = n_rows_per_class * ((n_classes + n_cols - 1) // n_cols)
    
    # Create the plot with proper sizing
    fig_width = max(15, n_cols * 1.2)
    fig_height = max(10, total_rows * 0.8)
    fig, axes = plt.subplots(total_rows, n_cols, figsize=(fig_width, fig_height))
    
    if epoch is not None:
        fig.suptitle(f'Generated Letter Samples - All Classes (Epoch {epoch})\\n{samples_per_class} samples per class', 
                     fontsize=16, fontweight='bold')
    else:
        fig.suptitle(f'Generated Letter Samples - All Classes\\n{samples_per_class} samples per class', 
                     fontsize=16, fontweight='bold')
    
    # Handle axes indexing
    if total_rows == 1:
        axes = axes.reshape(1, -1)
    elif n_cols == 1:
        axes = axes.reshape(-1, 1)
    
    # Initialize all axes as empty
    for i in range(total_rows):
        for j in range(n_cols):
            axes[i, j].axis('off')
    
    # Fill the grid with generated images
    for class_idx_enum, class_idx in enumerate(sorted(class_to_letter.keys())):
        letter = class_to_letter[class_idx]
        
        # Calculate which "class column" this belongs to
        class_col = class_idx_enum % n_cols
        class_row_start = (class_idx_enum // n_cols) * samples_per_class
        
        # Display all samples for this class
        for sample_idx in range(samples_per_class):
            row = class_row_start + sample_idx
            col = class_col
            
            if row < total_rows and col < n_cols:
                ax = axes[row, col]
                
                # Get the corresponding image
                img_idx = class_idx_enum * samples_per_class + sample_idx
                img = all_generated[img_idx].numpy().squeeze()
                
                # Display image
                ax.imshow(img, cmap='gray', vmin=-1, vmax=1)
                ax.axis('off')
                
                # Add class label on first sample of each class
                if sample_idx == 0:
                    ax.set_title(f'{letter} (Class {class_idx})', fontsize=10, fontweight='bold')
    
    plt.tight_layout()
    plt.show()
    
    return all_generated, all_labels


balanced_enhanced_trainer = BalancedGANTrainer(
    generator=enhanced_generator,
    discriminator=enhanced_discriminator,
    latent_dim=100,
    num_classes=num_classes
)

# =============================================================================
# ENHANCED DCGAN TRAINING - COMPLETE 50 EPOCH TRAINING
# =============================================================================

# Test visualization first
print("Testing visualization with current enhanced models...")
test_images, test_labels = display_generated_samples_grid(
    enhanced_generator, class_to_letter, samples_per_class=6
)

# Training configuration
NUM_EPOCHS = 50

start_time = time.time()

for epoch in range(NUM_EPOCHS):
    epoch_start = time.time()
    
    # Train for one epoch with balanced strategy
    balanced_enhanced_trainer.train_epoch(train_dataset, epoch, steps_per_epoch)
    
    # Display only on the final epoch
    if (epoch + 1) == NUM_EPOCHS:
        print(f"\nGenerating Grid of Samples at Final Epoch {epoch + 1}:")
        display_generated_samples_grid(enhanced_generator, class_to_letter, epoch + 1, samples_per_class=6)
    
    # Calculate and display epoch timing
    epoch_time = time.time() - epoch_start
    total_time = time.time() - start_time
    avg_time = total_time / (epoch + 1)
    eta = avg_time * (NUM_EPOCHS - epoch - 1)
    
total_training_time = time.time() - start_time


# Generate final display
print(f"\nFinal Generated Samples - All Letter Classes:")
final_images, final_labels = display_generated_samples_grid(
    enhanced_generator, class_to_letter, NUM_EPOCHS, samples_per_class=6
)

# Display comprehensive training summary
available_letters = sorted(class_to_letter.values())
missing_letters = [letter for letter in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' if letter not in available_letters]

# Plot training progress
if len(balanced_enhanced_trainer.history['gen_loss']) > 1:
    plt.figure(figsize=(15, 5))
    
    # Generator and Discriminator Loss
    plt.subplot(1, 3, 1)
    epochs = balanced_enhanced_trainer.history['epoch']
    plt.plot(epochs, balanced_enhanced_trainer.history['gen_loss'], label='Generator Loss', color='blue', linewidth=2)
    plt.plot(epochs, balanced_enhanced_trainer.history['disc_loss'], label='Discriminator Loss', color='red', linewidth=2)
    plt.title('Enhanced DCGAN - Training Losses', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Discriminator Accuracy
    plt.subplot(1, 3, 2)
    plt.plot(epochs, balanced_enhanced_trainer.history['label_accuracy'], label='Discriminator Accuracy', color='green', linewidth=2)
    plt.axhline(y=0.95, color='red', linestyle='--', alpha=0.7, label='Upper limit (95%)')
    plt.axhline(y=0.70, color='orange', linestyle='--', alpha=0.7, label='Lower limit (70%)')
    plt.title('Enhanced DCGAN - Discriminator Accuracy', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Loss Difference
    plt.subplot(1, 3, 3)
    loss_diff = [g - d for g, d in zip(balanced_enhanced_trainer.history['gen_loss'], balanced_enhanced_trainer.history['disc_loss'])]
    plt.plot(epochs, loss_diff, label='Gen Loss - Disc Loss', color='purple', linewidth=2)
    plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)
    plt.title('Enhanced DCGAN - Loss Balance', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss Difference')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()

Testing visualization with current enhanced models...
Generating 6 samples per class for all 16 letter classes...

Epoch 1
------------------------------------------------------------
Step    0/597 - Gen Loss: 5.3705, Disc Loss: 4.8662, Accuracy: 0.0625
Step  100/597 - Gen Loss: 4.6713, Disc Loss: 3.8841, Accuracy: 0.1569
Step  200/597 - Gen Loss: 3.9121, Disc Loss: 3.2987, Accuracy: 0.2537
Step  300/597 - Gen Loss: 3.1612, Disc Loss: 2.7788, Accuracy: 0.3339
Step  400/597 - Gen Loss: 2.7231, Disc Loss: 2.4758, Accuracy: 0.3888
Step  500/597 - Gen Loss: 2.4469, Disc Loss: 2.2765, Accuracy: 0.4304
Discriminator accuracy < 70% - discriminator might be too weak

Epoch 2
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.2643, Disc Loss: 1.2957, Accuracy: 0.6406
Step  100/597 - Gen Loss: 1.2673, Disc Loss: 1.3416, Accuracy: 0.6520
Step  200/597 - Gen Loss: 1.2671, Disc Loss: 1.3237, Accuracy: 0.6609
Step  300/597 - Gen Loss: 1.2632, Disc Loss: 1.3082, Accuracy: 0.6727
Step  400/597 - Gen Loss: 1.2650, Disc Loss: 1.2890, Accuracy: 0.6809
Step  500/597 - Gen Loss: 1.2673, Disc Loss: 1.2701, Accuracy: 0.6893
Discriminator accuracy < 70% - discriminator might be too weak

Epoch 3
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3010, Disc Loss: 1.0230, Accuracy: 0.7969
Step  100/597 - Gen Loss: 1.2846, Disc Loss: 1.1293, Accuracy: 0.7598
Step  200/597 - Gen Loss: 1.2917, Disc Loss: 1.1186, Accuracy: 0.7696
Step  300/597 - Gen Loss: 1.2915, Disc Loss: 1.1114, Accuracy: 0.7747
Step  400/597 - Gen Loss: 1.2929, Disc Loss: 1.0991, Accuracy: 0.7866
Step  500/597 - Gen Loss: 1.2893, Disc Loss: 1.0904, Accuracy: 0.7936
Discriminator accuracy in good range (70-95%)

Epoch 4
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.2726, Disc Loss: 1.0860, Accuracy: 0.8281
Step  100/597 - Gen Loss: 1.2611, Disc Loss: 0.9966, Accuracy: 0.8456
Step  200/597 - Gen Loss: 1.2625, Disc Loss: 0.9846, Accuracy: 0.8543
Step  300/597 - Gen Loss: 1.2532, Disc Loss: 0.9714, Accuracy: 0.8607
Step  400/597 - Gen Loss: 1.2542, Disc Loss: 0.9660, Accuracy: 0.8658
Step  500/597 - Gen Loss: 1.2502, Disc Loss: 0.9565, Accuracy: 0.8720
Discriminator accuracy in good range (70-95%)

Epoch 5
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1247, Disc Loss: 0.8879, Accuracy: 0.9219
Step  100/597 - Gen Loss: 1.2221, Disc Loss: 0.8865, Accuracy: 0.9182
Step  200/597 - Gen Loss: 1.2170, Disc Loss: 0.8829, Accuracy: 0.9183
Step  300/597 - Gen Loss: 1.2154, Disc Loss: 0.8759, Accuracy: 0.9229
Step  400/597 - Gen Loss: 1.2146, Disc Loss: 0.8732, Accuracy: 0.9244
Step  500/597 - Gen Loss: 1.2093, Disc Loss: 0.8669, Accuracy: 0.9278
Discriminator accuracy in good range (70-95%)

Epoch 6
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.2253, Disc Loss: 0.8272, Accuracy: 0.9375
Step  100/597 - Gen Loss: 1.1802, Disc Loss: 0.8378, Accuracy: 0.9482
Step  200/597 - Gen Loss: 1.1702, Disc Loss: 0.8371, Accuracy: 0.9472
Step  300/597 - Gen Loss: 1.1654, Disc Loss: 0.8301, Accuracy: 0.9500
Step  400/597 - Gen Loss: 1.1641, Disc Loss: 0.8238, Accuracy: 0.9520
Step  500/597 - Gen Loss: 1.1620, Disc Loss: 0.8195, Accuracy: 0.9547
Discriminator accuracy > 95% - consider further balancing

Epoch 7
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1750, Disc Loss: 0.7238, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.1405, Disc Loss: 0.7797, Accuracy: 0.9715
Step  200/597 - Gen Loss: 1.1431, Disc Loss: 0.7810, Accuracy: 0.9711
Step  300/597 - Gen Loss: 1.1384, Disc Loss: 0.7781, Accuracy: 0.9725
Step  400/597 - Gen Loss: 1.1377, Disc Loss: 0.7773, Accuracy: 0.9734
Step  500/597 - Gen Loss: 1.1347, Disc Loss: 0.7759, Accuracy: 0.9747
Discriminator accuracy > 95% - consider further balancing

Epoch 8
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.2012, Disc Loss: 0.7469, Accuracy: 0.9844
Step  100/597 - Gen Loss: 1.1195, Disc Loss: 0.7566, Accuracy: 0.9856
Step  200/597 - Gen Loss: 1.1184, Disc Loss: 0.7601, Accuracy: 0.9839
Step  300/597 - Gen Loss: 1.1173, Disc Loss: 0.7579, Accuracy: 0.9846
Step  400/597 - Gen Loss: 1.1128, Disc Loss: 0.7556, Accuracy: 0.9843
Step  500/597 - Gen Loss: 1.1115, Disc Loss: 0.7531, Accuracy: 0.9852
Discriminator accuracy > 95% - consider further balancing

Epoch 9
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1017, Disc Loss: 0.7205, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0952, Disc Loss: 0.7417, Accuracy: 0.9896
Step  200/597 - Gen Loss: 1.0976, Disc Loss: 0.7400, Accuracy: 0.9901
Step  300/597 - Gen Loss: 1.0964, Disc Loss: 0.7371, Accuracy: 0.9911
Step  400/597 - Gen Loss: 1.0944, Disc Loss: 0.7361, Accuracy: 0.9907
Step  500/597 - Gen Loss: 1.0914, Disc Loss: 0.7352, Accuracy: 0.9911
Discriminator accuracy > 95% - consider further balancing

Epoch 10
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0304, Disc Loss: 0.6985, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0767, Disc Loss: 0.7306, Accuracy: 0.9923
Step  200/597 - Gen Loss: 1.0789, Disc Loss: 0.7300, Accuracy: 0.9933
Step  300/597 - Gen Loss: 1.0802, Disc Loss: 0.7293, Accuracy: 0.9934
Step  400/597 - Gen Loss: 1.0803, Disc Loss: 0.7285, Accuracy: 0.9932
Step  500/597 - Gen Loss: 1.0783, Disc Loss: 0.7270, Accuracy: 0.9932
Discriminator accuracy > 95% - consider further balancing

Epoch 11
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0526, Disc Loss: 0.7288, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0714, Disc Loss: 0.7229, Accuracy: 0.9957
Step  200/597 - Gen Loss: 1.0666, Disc Loss: 0.7225, Accuracy: 0.9947
Step  300/597 - Gen Loss: 1.0681, Disc Loss: 0.7220, Accuracy: 0.9954
Step  400/597 - Gen Loss: 1.0685, Disc Loss: 0.7210, Accuracy: 0.9958
Step  500/597 - Gen Loss: 1.0671, Disc Loss: 0.7201, Accuracy: 0.9956
Discriminator accuracy > 95% - consider further balancing

Epoch 12
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0621, Disc Loss: 0.7100, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0654, Disc Loss: 0.7138, Accuracy: 0.9942
Step  200/597 - Gen Loss: 1.0681, Disc Loss: 0.7143, Accuracy: 0.9955
Step  300/597 - Gen Loss: 1.0673, Disc Loss: 0.7139, Accuracy: 0.9959
Step  400/597 - Gen Loss: 1.0689, Disc Loss: 0.7137, Accuracy: 0.9961
Step  500/597 - Gen Loss: 1.0670, Disc Loss: 0.7131, Accuracy: 0.9963
Discriminator accuracy > 95% - consider further balancing

Epoch 13
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0717, Disc Loss: 0.7038, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0655, Disc Loss: 0.7077, Accuracy: 0.9988
Step  200/597 - Gen Loss: 1.0658, Disc Loss: 0.7104, Accuracy: 0.9972
Step  300/597 - Gen Loss: 1.0652, Disc Loss: 0.7106, Accuracy: 0.9974
Step  400/597 - Gen Loss: 1.0635, Disc Loss: 0.7097, Accuracy: 0.9976
Step  500/597 - Gen Loss: 1.0621, Disc Loss: 0.7094, Accuracy: 0.9978
Discriminator accuracy > 95% - consider further balancing

Epoch 14
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0717, Disc Loss: 0.7057, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0562, Disc Loss: 0.7077, Accuracy: 0.9975
Step  200/597 - Gen Loss: 1.0575, Disc Loss: 0.7071, Accuracy: 0.9981
Step  300/597 - Gen Loss: 1.0545, Disc Loss: 0.7066, Accuracy: 0.9980
Step  400/597 - Gen Loss: 1.0547, Disc Loss: 0.7067, Accuracy: 0.9982
Step  500/597 - Gen Loss: 1.0538, Disc Loss: 0.7066, Accuracy: 0.9981
Discriminator accuracy > 95% - consider further balancing

Epoch 15
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0361, Disc Loss: 0.7016, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0508, Disc Loss: 0.7048, Accuracy: 0.9994
Step  200/597 - Gen Loss: 1.0515, Disc Loss: 0.7055, Accuracy: 0.9992
Step  300/597 - Gen Loss: 1.0510, Disc Loss: 0.7057, Accuracy: 0.9987
Step  400/597 - Gen Loss: 1.0520, Disc Loss: 0.7053, Accuracy: 0.9985
Step  500/597 - Gen Loss: 1.0509, Disc Loss: 0.7048, Accuracy: 0.9987
Discriminator accuracy > 95% - consider further balancing

Epoch 16
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0673, Disc Loss: 0.7062, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0546, Disc Loss: 0.7046, Accuracy: 0.9997
Step  200/597 - Gen Loss: 1.0532, Disc Loss: 0.7028, Accuracy: 0.9994
Step  300/597 - Gen Loss: 1.0521, Disc Loss: 0.7027, Accuracy: 0.9994
Step  400/597 - Gen Loss: 1.0516, Disc Loss: 0.7028, Accuracy: 0.9992
Step  500/597 - Gen Loss: 1.0513, Disc Loss: 0.7025, Accuracy: 0.9992
Discriminator accuracy > 95% - consider further balancing

Epoch 17
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0353, Disc Loss: 0.7014, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0431, Disc Loss: 0.7019, Accuracy: 0.9997
Step  200/597 - Gen Loss: 1.0447, Disc Loss: 0.7017, Accuracy: 0.9997
Step  300/597 - Gen Loss: 1.0464, Disc Loss: 0.7012, Accuracy: 0.9995
Step  400/597 - Gen Loss: 1.0462, Disc Loss: 0.7009, Accuracy: 0.9994
Step  500/597 - Gen Loss: 1.0457, Disc Loss: 0.7003, Accuracy: 0.9995
Discriminator accuracy > 95% - consider further balancing

Epoch 18
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0646, Disc Loss: 0.7028, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0435, Disc Loss: 0.7022, Accuracy: 0.9994
Step  200/597 - Gen Loss: 1.0424, Disc Loss: 0.7011, Accuracy: 0.9997
Step  300/597 - Gen Loss: 1.0440, Disc Loss: 0.7006, Accuracy: 0.9997
Step  400/597 - Gen Loss: 1.0442, Disc Loss: 0.7006, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0439, Disc Loss: 0.7004, Accuracy: 0.9998
Discriminator accuracy > 95% - consider further balancing

Epoch 19
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0519, Disc Loss: 0.7129, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0439, Disc Loss: 0.6986, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0436, Disc Loss: 0.6996, Accuracy: 0.9997
Step  300/597 - Gen Loss: 1.0415, Disc Loss: 0.7000, Accuracy: 0.9994
Step  400/597 - Gen Loss: 1.0412, Disc Loss: 0.6998, Accuracy: 0.9995
Step  500/597 - Gen Loss: 1.0431, Disc Loss: 0.6995, Accuracy: 0.9996
Discriminator accuracy > 95% - consider further balancing

Epoch 20
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0527, Disc Loss: 0.6998, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0412, Disc Loss: 0.6976, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0435, Disc Loss: 0.6983, Accuracy: 0.9998
Step  300/597 - Gen Loss: 1.0436, Disc Loss: 0.6984, Accuracy: 0.9998
Step  400/597 - Gen Loss: 1.0438, Disc Loss: 0.6983, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0441, Disc Loss: 0.6982, Accuracy: 0.9998
Discriminator accuracy > 95% - consider further balancing

Epoch 21
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0196, Disc Loss: 0.6986, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0444, Disc Loss: 0.6968, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0415, Disc Loss: 0.6964, Accuracy: 0.9997
Step  300/597 - Gen Loss: 1.0409, Disc Loss: 0.6970, Accuracy: 0.9997
Step  400/597 - Gen Loss: 1.0413, Disc Loss: 0.6968, Accuracy: 0.9997
Step  500/597 - Gen Loss: 1.0408, Disc Loss: 0.6969, Accuracy: 0.9997
Discriminator accuracy > 95% - consider further balancing

Epoch 22
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0265, Disc Loss: 0.7005, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0384, Disc Loss: 0.6961, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0396, Disc Loss: 0.6962, Accuracy: 0.9998
Step  300/597 - Gen Loss: 1.0391, Disc Loss: 0.6966, Accuracy: 0.9998
Step  400/597 - Gen Loss: 1.0385, Disc Loss: 0.6968, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0392, Disc Loss: 0.6969, Accuracy: 0.9998
Discriminator accuracy > 95% - consider further balancing

Epoch 23
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0718, Disc Loss: 0.6942, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0433, Disc Loss: 0.6962, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0419, Disc Loss: 0.6962, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0400, Disc Loss: 0.6965, Accuracy: 0.9999
Step  400/597 - Gen Loss: 1.0396, Disc Loss: 0.6967, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0389, Disc Loss: 0.6967, Accuracy: 0.9999
Discriminator accuracy > 95% - consider further balancing

Epoch 24
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0402, Disc Loss: 0.6985, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0394, Disc Loss: 0.6961, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0396, Disc Loss: 0.6961, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0389, Disc Loss: 0.6962, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0389, Disc Loss: 0.6963, Accuracy: 0.9999
Step  500/597 - Gen Loss: 1.0389, Disc Loss: 0.6963, Accuracy: 0.9999
Discriminator accuracy > 95% - consider further balancing

Epoch 25
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0536, Disc Loss: 0.6964, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0454, Disc Loss: 0.6958, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0434, Disc Loss: 0.6964, Accuracy: 0.9997
Step  300/597 - Gen Loss: 1.0424, Disc Loss: 0.6961, Accuracy: 0.9998
Step  400/597 - Gen Loss: 1.0416, Disc Loss: 0.6959, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0412, Disc Loss: 0.6959, Accuracy: 0.9999
Discriminator accuracy > 95% - consider further balancing

Epoch 26
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0490, Disc Loss: 0.6999, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0371, Disc Loss: 0.6955, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0373, Disc Loss: 0.6954, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0376, Disc Loss: 0.6958, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0376, Disc Loss: 0.6958, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0381, Disc Loss: 0.6956, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 27
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0314, Disc Loss: 0.6988, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0383, Disc Loss: 0.6949, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0383, Disc Loss: 0.6950, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0390, Disc Loss: 0.6947, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0385, Disc Loss: 0.6948, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0382, Disc Loss: 0.6947, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 28
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0408, Disc Loss: 0.6919, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0369, Disc Loss: 0.6951, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0366, Disc Loss: 0.6952, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0363, Disc Loss: 0.6952, Accuracy: 0.9999
Step  400/597 - Gen Loss: 1.0362, Disc Loss: 0.6953, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0362, Disc Loss: 0.6952, Accuracy: 0.9999
Discriminator accuracy > 95% - consider further balancing

Epoch 29
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0469, Disc Loss: 0.6961, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0384, Disc Loss: 0.6956, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0376, Disc Loss: 0.6958, Accuracy: 0.9994
Step  300/597 - Gen Loss: 1.0396, Disc Loss: 0.6956, Accuracy: 0.9996
Step  400/597 - Gen Loss: 1.0395, Disc Loss: 0.6954, Accuracy: 0.9997
Step  500/597 - Gen Loss: 1.0390, Disc Loss: 0.6954, Accuracy: 0.9998
Discriminator accuracy > 95% - consider further balancing

Epoch 30
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0403, Disc Loss: 0.6909, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0407, Disc Loss: 0.6949, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0377, Disc Loss: 0.6946, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0385, Disc Loss: 0.6946, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0390, Disc Loss: 0.6946, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0387, Disc Loss: 0.6946, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 31
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0303, Disc Loss: 0.6999, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0371, Disc Loss: 0.6941, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0386, Disc Loss: 0.6944, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0382, Disc Loss: 0.6942, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0382, Disc Loss: 0.6944, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0380, Disc Loss: 0.6946, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 32
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0376, Disc Loss: 0.6987, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0400, Disc Loss: 0.6948, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0403, Disc Loss: 0.6948, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0400, Disc Loss: 0.6947, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0398, Disc Loss: 0.6946, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0394, Disc Loss: 0.6944, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 33
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0348, Disc Loss: 0.6958, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0382, Disc Loss: 0.6950, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0377, Disc Loss: 0.6946, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0377, Disc Loss: 0.6943, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0374, Disc Loss: 0.6942, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0368, Disc Loss: 0.6942, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 34
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0542, Disc Loss: 0.6898, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0390, Disc Loss: 0.6946, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0389, Disc Loss: 0.6944, Accuracy: 0.9998
Step  300/597 - Gen Loss: 1.0377, Disc Loss: 0.6945, Accuracy: 0.9999
Step  400/597 - Gen Loss: 1.0380, Disc Loss: 0.6945, Accuracy: 0.9998
Step  500/597 - Gen Loss: 1.0381, Disc Loss: 0.6945, Accuracy: 0.9999
Discriminator accuracy > 95% - consider further balancing

Epoch 35
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0477, Disc Loss: 0.6901, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0409, Disc Loss: 0.6941, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0391, Disc Loss: 0.6943, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0389, Disc Loss: 0.6946, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0381, Disc Loss: 0.6946, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0379, Disc Loss: 0.6945, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 36
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0344, Disc Loss: 0.6949, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0416, Disc Loss: 0.6938, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0397, Disc Loss: 0.6943, Accuracy: 0.9998
Step  300/597 - Gen Loss: 1.0394, Disc Loss: 0.6945, Accuracy: 0.9999
Step  400/597 - Gen Loss: 1.0391, Disc Loss: 0.6944, Accuracy: 0.9999
Step  500/597 - Gen Loss: 1.0384, Disc Loss: 0.6944, Accuracy: 0.9999
Discriminator accuracy > 95% - consider further balancing

Epoch 37
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0308, Disc Loss: 0.6962, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0370, Disc Loss: 0.6943, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0374, Disc Loss: 0.6944, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0378, Disc Loss: 0.6943, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0388, Disc Loss: 0.6943, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0385, Disc Loss: 0.6943, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 38
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0428, Disc Loss: 0.6954, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0394, Disc Loss: 0.6945, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0379, Disc Loss: 0.6942, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0378, Disc Loss: 0.6942, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0380, Disc Loss: 0.6940, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0375, Disc Loss: 0.6940, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 39
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0369, Disc Loss: 0.6946, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0391, Disc Loss: 0.6942, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0375, Disc Loss: 0.6944, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0384, Disc Loss: 0.6942, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0382, Disc Loss: 0.6942, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0382, Disc Loss: 0.6942, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 40
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0430, Disc Loss: 0.6961, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0396, Disc Loss: 0.6943, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0374, Disc Loss: 0.6945, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0373, Disc Loss: 0.6942, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0372, Disc Loss: 0.6941, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0371, Disc Loss: 0.6942, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 41
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0425, Disc Loss: 0.6948, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0393, Disc Loss: 0.6943, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0386, Disc Loss: 0.6941, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0379, Disc Loss: 0.6941, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0376, Disc Loss: 0.6941, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0382, Disc Loss: 0.6942, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 42
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0422, Disc Loss: 0.6933, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0396, Disc Loss: 0.6944, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0373, Disc Loss: 0.6941, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0383, Disc Loss: 0.6942, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0385, Disc Loss: 0.6942, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0383, Disc Loss: 0.6941, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 43
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0482, Disc Loss: 0.6915, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0398, Disc Loss: 0.6939, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0381, Disc Loss: 0.6940, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0386, Disc Loss: 0.6940, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0383, Disc Loss: 0.6940, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0381, Disc Loss: 0.6940, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 44
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0555, Disc Loss: 0.6914, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0367, Disc Loss: 0.6942, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0384, Disc Loss: 0.6942, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0384, Disc Loss: 0.6942, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0382, Disc Loss: 0.6941, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0373, Disc Loss: 0.6941, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 45
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0300, Disc Loss: 0.6946, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0341, Disc Loss: 0.6941, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0356, Disc Loss: 0.6941, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0366, Disc Loss: 0.6939, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0370, Disc Loss: 0.6940, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0373, Disc Loss: 0.6940, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 46
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0367, Disc Loss: 0.6928, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0391, Disc Loss: 0.6937, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0385, Disc Loss: 0.6938, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0375, Disc Loss: 0.6938, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0376, Disc Loss: 0.6939, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0371, Disc Loss: 0.6939, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 47
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0415, Disc Loss: 0.6920, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0351, Disc Loss: 0.6943, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0362, Disc Loss: 0.6942, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0362, Disc Loss: 0.6941, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0368, Disc Loss: 0.6941, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0373, Disc Loss: 0.6941, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 48
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0347, Disc Loss: 0.6917, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0378, Disc Loss: 0.6939, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0374, Disc Loss: 0.6941, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0377, Disc Loss: 0.6939, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0373, Disc Loss: 0.6940, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0372, Disc Loss: 0.6940, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 49
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0373, Disc Loss: 0.6911, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0376, Disc Loss: 0.6942, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0373, Disc Loss: 0.6939, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0372, Disc Loss: 0.6940, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0367, Disc Loss: 0.6940, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0371, Disc Loss: 0.6940, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Epoch 50
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0386, Disc Loss: 0.6944, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.0358, Disc Loss: 0.6939, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.0376, Disc Loss: 0.6941, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0371, Disc Loss: 0.6941, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0372, Disc Loss: 0.6941, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0372, Disc Loss: 0.6940, Accuracy: 1.0000
Discriminator accuracy > 95% - consider further balancing

Generating Grid of Samples at Final Epoch 50:
Generating 6 samples per class for all 16 letter classes...

Final Generated Samples - All Letter Classes:
Generating 6 samples per class for all 16 letter classes...

# =============================================================================
# WGAN (WASSERSTEIN GAN) IMPLEMENTATION
# =============================================================================

# Baseline WGAN Critic
def build_baseline_wgan_critic(img_height=28, img_width=28, num_classes=16):
    """Build baseline WGAN critic (discriminator without sigmoid)"""
    
    # Image input
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    
    # Label input  
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process image
    x = img_input
    
    # First conv block (28x28x1 -> 14x14x64)
    x = tf.keras.layers.Conv2D(64, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    
    # Second conv block (14x14x64 -> 7x7x128)
    x = tf.keras.layers.Conv2D(128, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    
    # Third conv block (7x7x128 -> 4x4x256)
    x = tf.keras.layers.Conv2D(256, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    
    # Flatten for dense layers
    x = tf.keras.layers.Flatten()(x)
    
    # Process label
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate image features and label
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Dense layers
    x = tf.keras.layers.Dense(512)(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    
    # Output layer - NO SIGMOID for WGAN (raw scores)
    validity = tf.keras.layers.Dense(1, name='validity')(x)  # No activation!
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='baseline_wgan_critic'
    )
    
    return model

# Baseline WGAN Generator (same as DCGAN baseline)
def build_baseline_wgan_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """Build baseline WGAN generator"""
    
    # Noise input
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    
    # Label input
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process label
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate noise and label
    x = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Dense layer to reshape
    x = tf.keras.layers.Dense(7 * 7 * 256, use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    x = tf.keras.layers.Reshape((7, 7, 256))(x)
    
    # First transpose conv (7x7x256 -> 14x14x128)
    x = tf.keras.layers.Conv2DTranspose(128, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    
    # Second transpose conv (14x14x128 -> 28x28x1)
    x = tf.keras.layers.Conv2DTranspose(1, 5, strides=2, padding='same', use_bias=False, activation='tanh')(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='baseline_wgan_generator'
    )
    
    return model

# Enhanced WGAN Critic with deeper architecture
def build_enhanced_wgan_critic(img_height=28, img_width=28, num_classes=16):
    """Build enhanced WGAN critic with deeper architecture"""
    
    # Image input
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    
    # Label input
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process image with deeper architecture
    x = img_input
    
    # First conv block (28x28x1 -> 14x14x64)
    x = tf.keras.layers.Conv2D(64, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Second conv block (14x14x64 -> 7x7x128)
    x = tf.keras.layers.Conv2D(128, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Third conv block (7x7x128 -> 4x4x256)
    x = tf.keras.layers.Conv2D(256, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Fourth conv block (4x4x256 -> 2x2x512) - Enhanced depth
    x = tf.keras.layers.Conv2D(512, 4, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.4)(x)
    
    # Flatten for dense layers
    x = tf.keras.layers.Flatten()(x)
    
    # Process label
    label_embedding = tf.keras.layers.Embedding(num_classes, 100)(label_input)  # Larger embedding
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate image features and label
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Enhanced dense layers
    x = tf.keras.layers.Dense(1024)(x)  # Larger capacity
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    x = tf.keras.layers.Dense(512)(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Output layers - NO SIGMOID for WGAN
    validity = tf.keras.layers.Dense(1, name='validity')(x)  # Raw scores
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='enhanced_wgan_critic'
    )
    
    return model

# Enhanced WGAN Generator with deeper architecture
def build_enhanced_wgan_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """Build enhanced WGAN generator with deeper architecture"""
    
    # Noise input
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    
    # Label input
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process label with larger embedding
    label_embedding = tf.keras.layers.Embedding(num_classes, 100)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate noise and label
    x = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Enhanced dense preprocessing
    x = tf.keras.layers.Dense(4 * 4 * 1024, use_bias=False)(x)  # Start smaller, go deeper
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    x = tf.keras.layers.Reshape((4, 4, 1024))(x)
    
    # First transpose conv (4x4x1024 -> 7x7x512)
    x = tf.keras.layers.Conv2DTranspose(512, 4, strides=1, padding='valid', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    
    # Second transpose conv (7x7x512 -> 14x14x256)
    x = tf.keras.layers.Conv2DTranspose(256, 4, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    
    # Third transpose conv (14x14x256 -> 28x28x128)
    x = tf.keras.layers.Conv2DTranspose(128, 4, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    
    # Final layer (28x28x128 -> 28x28x1)
    x = tf.keras.layers.Conv2DTranspose(1, 4, strides=1, padding='same', use_bias=False, activation='tanh')(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='enhanced_wgan_generator'
    )
    
    return model

baseline_wgan_generator = build_baseline_wgan_generator(
    latent_dim=100, 
    num_classes=num_classes,
    img_height=28, 
    img_width=28
)

baseline_wgan_critic = build_baseline_wgan_critic(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes
)

# Build Enhanced WGAN models
enhanced_wgan_generator = build_enhanced_wgan_generator(
    latent_dim=100, 
    num_classes=num_classes,
    img_height=28, 
    img_width=28
)

enhanced_wgan_critic = build_enhanced_wgan_critic(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes
)

# =============================================================================
# WGAN TRAINER CLASS WITH GRADIENT PENALTY
# =============================================================================

class WGANTrainer:
    """WGAN-GP trainer with gradient penalty for stable training"""
    
    def __init__(self, generator, critic, latent_dim=100, num_classes=16, gradient_penalty_weight=10.0):
        self.generator = generator
        self.critic = critic
        self.latent_dim = latent_dim
        self.num_classes = num_classes
        self.gradient_penalty_weight = gradient_penalty_weight
        
        # Optimizers - different learning rates for generator and critic
        self.gen_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001, beta_1=0.5, beta_2=0.9)
        self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0004, beta_1=0.5, beta_2=0.9)
        
        # Loss function for label classification
        self.categorical_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
        
        # Training parameters
        self.n_critic = 5  # Train critic 5 times per generator training
        
        # Metrics for tracking
        self.gen_loss_metric = tf.keras.metrics.Mean(name='gen_loss')
        self.critic_loss_metric = tf.keras.metrics.Mean(name='critic_loss')
        self.wasserstein_loss_metric = tf.keras.metrics.Mean(name='wasserstein_loss')
        self.gp_loss_metric = tf.keras.metrics.Mean(name='gp_loss')
        self.acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(name='label_accuracy')
        
        # Training history
        self.history = {
            'gen_loss': [],
            'critic_loss': [],
            'wasserstein_loss': [],
            'gp_loss': [],
            'label_accuracy': [],
            'epoch': []
        }
    
    def gradient_penalty(self, real_images, fake_images, real_labels):
        """Calculate gradient penalty for WGAN-GP"""
        batch_size = tf.shape(real_images)[0]
        
        # Random interpolation factor
        alpha = tf.random.uniform([batch_size, 1, 1, 1], 0.0, 1.0)
        
        # Interpolate between real and fake images
        interpolated = alpha * real_images + (1 - alpha) * fake_images
        
        with tf.GradientTape() as tape:
            tape.watch(interpolated)
            # Get critic scores for interpolated images
            critic_output, _ = self.critic([interpolated, real_labels], training=True)
        
        # Calculate gradients of critic output w.r.t. interpolated images
        gradients = tape.gradient(critic_output, interpolated)
        
        # Calculate gradient norm
        gradients_norm = tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=[1, 2, 3]))
        
        # Gradient penalty: (||grad|| - 1)^2
        gradient_penalty = tf.reduce_mean(tf.square(gradients_norm - 1.0))
        
        return gradient_penalty
    
    @tf.function
    def train_critic(self, real_images, real_labels, batch_size):
        """Train critic with Wasserstein loss and gradient penalty"""
        # Generate fake images
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], 0, self.num_classes, dtype=tf.int32)
        fake_images = self.generator([noise, fake_labels], training=True)
        
        with tf.GradientTape() as tape:
            # Get critic scores for real images
            real_validity, real_label_pred = self.critic([real_images, real_labels], training=True)
            
            # Get critic scores for fake images
            fake_validity, fake_label_pred = self.critic([fake_images, fake_labels], training=True)
            
            # Wasserstein loss: maximize D(real) - D(fake)
            # For minimization: minimize -(D(real) - D(fake)) = minimize (D(fake) - D(real))
            wasserstein_loss = tf.reduce_mean(fake_validity) - tf.reduce_mean(real_validity)
            
            # Gradient penalty
            gp_loss = self.gradient_penalty(real_images, fake_images, real_labels)
            
            # Label classification losses
            real_label_loss = self.categorical_loss(real_labels, real_label_pred)
            fake_label_loss = self.categorical_loss(fake_labels, fake_label_pred)
            label_loss = (real_label_loss + fake_label_loss) / 2
            
            # Total critic loss
            critic_loss = wasserstein_loss + self.gradient_penalty_weight * gp_loss + label_loss
        
        # Update critic
        gradients = tape.gradient(critic_loss, self.critic.trainable_variables)
        self.critic_optimizer.apply_gradients(zip(gradients, self.critic.trainable_variables))
        
        # Update metrics
        self.critic_loss_metric.update_state(critic_loss)
        self.wasserstein_loss_metric.update_state(wasserstein_loss)
        self.gp_loss_metric.update_state(gp_loss)
        self.acc_metric.update_state(real_labels, real_label_pred)
        
        return critic_loss, wasserstein_loss, gp_loss
    
    @tf.function
    def train_generator(self, batch_size):
        """Train generator to fool the critic"""
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], 0, self.num_classes, dtype=tf.int32)
        
        with tf.GradientTape() as tape:
            fake_images = self.generator([noise, fake_labels], training=True)
            fake_validity, fake_label_pred = self.critic([fake_images, fake_labels], training=True)
            
            # Generator wants to maximize critic score for fake images
            # For minimization: minimize -D(fake)
            adversarial_loss = -tf.reduce_mean(fake_validity)
            
            # Generator wants correct label classification
            label_loss = self.categorical_loss(fake_labels, fake_label_pred)
            
            # Total generator loss
            gen_loss = adversarial_loss + label_loss
        
        # Update generator
        gradients = tape.gradient(gen_loss, self.generator.trainable_variables)
        self.gen_optimizer.apply_gradients(zip(gradients, self.generator.trainable_variables))
        
        # Update metrics
        self.gen_loss_metric.update_state(gen_loss)
        
        return gen_loss
    
    def train_epoch(self, dataset, epoch, steps_per_epoch):
        """Train for one epoch with WGAN-GP strategy"""
        print(f"\nEpoch {epoch + 1}")
        print("-" * 60)
        
        # Reset metrics
        self.gen_loss_metric.reset_states()
        self.critic_loss_metric.reset_states()
        self.wasserstein_loss_metric.reset_states()
        self.gp_loss_metric.reset_states()
        self.acc_metric.reset_states()
        
        # Training loop
        for step, (real_images, real_labels) in enumerate(dataset.take(steps_per_epoch)):
            batch_size = tf.shape(real_images)[0]
            
            # Train critic multiple times per generator training
            for _ in range(self.n_critic):
                critic_loss, wasserstein_loss, gp_loss = self.train_critic(real_images, real_labels, batch_size)
            
            # Train generator once
            gen_loss = self.train_generator(batch_size)
            
            # Print progress
            if step % 100 == 0:
                print(f"Step {step:4d}/{steps_per_epoch} - "
                      f"Gen Loss: {self.gen_loss_metric.result():.4f}, "
                      f"Critic Loss: {self.critic_loss_metric.result():.4f}, "
                      f"W-Loss: {self.wasserstein_loss_metric.result():.4f}, "
                      f"GP: {self.gp_loss_metric.result():.4f}, "
                      f"Acc: {self.acc_metric.result():.4f}")
        
        # Store epoch results
        self.history['gen_loss'].append(float(self.gen_loss_metric.result()))
        self.history['critic_loss'].append(float(self.critic_loss_metric.result()))
        self.history['wasserstein_loss'].append(float(self.wasserstein_loss_metric.result()))
        self.history['gp_loss'].append(float(self.gp_loss_metric.result()))
        self.history['label_accuracy'].append(float(self.acc_metric.result()))
        self.history['epoch'].append(epoch)
        

# Baseline WGAN trainer
baseline_wgan_trainer_new = WGANTrainer(
    generator=baseline_wgan_generator,
    critic=baseline_wgan_critic,
    latent_dim=100,
    num_classes=num_classes,
    gradient_penalty_weight=10.0
)

# Enhanced WGAN trainer
enhanced_wgan_trainer_new = WGANTrainer(
    generator=enhanced_wgan_generator,
    critic=enhanced_wgan_critic,
    latent_dim=100,
    num_classes=num_classes,
    gradient_penalty_weight=10.0
)

# ============================================================================= 
# BASELINE WGAN TRAINING 
# =============================================================================

# Test visualization first
print("Testing visualization with baseline WGAN models...")
test_images_baseline, test_labels_baseline = display_generated_samples_grid(
    baseline_wgan_generator, class_to_letter, samples_per_class=6
)

# Training configuration
NUM_EPOCHS = 50

start_time = time.time()

for epoch in range(NUM_EPOCHS):
    epoch_start = time.time()
    
    # Train for one epoch with WGAN-GP strategy
    baseline_wgan_trainer_new.train_epoch(train_dataset, epoch, steps_per_epoch)
    
    # Display only on the final epoch
    if (epoch + 1) == NUM_EPOCHS:
        print(f"\nGenerating Grid of Samples at Final Epoch {epoch + 1}:")
        display_generated_samples_grid(baseline_wgan_generator, class_to_letter, epoch + 1, samples_per_class=6)
    
    # Calculate and display epoch timing
    epoch_time = time.time() - epoch_start
    total_time = time.time() - start_time
    avg_time = total_time / (epoch + 1)
    eta = avg_time * (NUM_EPOCHS - epoch - 1)

    print("-" * 70)

total_training_time = time.time() - start_time

# Generate final display
print(f"\nFinal Generated Samples - All Letter Classes:")
final_images_baseline, final_labels_baseline = display_generated_samples_grid(
    baseline_wgan_generator, class_to_letter, NUM_EPOCHS, samples_per_class=6
)

# Display comprehensive training summary
available_letters = sorted(class_to_letter.values())
missing_letters = [letter for letter in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' if letter not in available_letters]

# Plot training progress
if len(baseline_wgan_trainer_new.history['gen_loss']) > 1:
    plt.figure(figsize=(15, 5))
    
    # Generator and Critic Loss
    plt.subplot(1, 3, 1)
    epochs = baseline_wgan_trainer_new.history['epoch']
    plt.plot(epochs, baseline_wgan_trainer_new.history['gen_loss'], label='Generator Loss', color='blue', linewidth=2)
    plt.plot(epochs, baseline_wgan_trainer_new.history['critic_loss'], label='Critic Loss', color='red', linewidth=2)
    plt.title('Baseline WGAN - Training Losses', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Critic Accuracy
    plt.subplot(1, 3, 2)
    plt.plot(epochs, baseline_wgan_trainer_new.history['label_accuracy'], label='Critic Accuracy', color='green', linewidth=2)
    plt.axhline(y=0.95, color='red', linestyle='--', alpha=0.7, label='Upper limit (95%)')
    plt.axhline(y=0.70, color='orange', linestyle='--', alpha=0.7, label='Lower limit (70%)')
    plt.title('Baseline WGAN - Critic Accuracy', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Loss Difference
    plt.subplot(1, 3, 3)
    loss_diff = [g - c for g, c in zip(baseline_wgan_trainer_new.history['gen_loss'], baseline_wgan_trainer_new.history['critic_loss'])]
    plt.plot(epochs, loss_diff, label='Gen Loss - Critic Loss', color='purple', linewidth=2)
    plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)
    plt.title('Baseline WGAN - Loss Balance', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss Difference')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()

Testing visualization with baseline WGAN models...
Generating 6 samples per class for all 16 letter classes...

Epoch 1
------------------------------------------------------------
Step    0/597 - Gen Loss: 8.6118, Critic Loss: -0.0681, W-Loss: -7.1122, GP: 0.4398, Acc: 0.3625
Step  100/597 - Gen Loss: 18.3094, Critic Loss: -131.0980, W-Loss: -216.4599, GP: 8.4363, Acc: 0.8805
Step  200/597 - Gen Loss: -5.7413, Critic Loss: -111.7363, W-Loss: -195.4327, GP: 8.3175, Acc: 0.9400
Step  300/597 - Gen Loss: -23.6389, Critic Loss: -147.2175, W-Loss: -245.1508, GP: 9.7581, Acc: 0.9599
Step  400/597 - Gen Loss: -25.3361, Critic Loss: -288.6538, W-Loss: -442.6199, GP: 15.3698, Acc: 0.9692
Step  500/597 - Gen Loss: -10.2545, Critic Loss: -589.1346, W-Loss: -858.3094, GP: 26.8951, Acc: 0.9723
----------------------------------------------------------------------

Epoch 2
------------------------------------------------------------
Step    0/597 - Gen Loss: 785.8337, Critic Loss: -4799.9863, W-Loss: -5823.1958, GP: 102.3207, Acc: 1.0000
Step  100/597 - Gen Loss: 1369.5128, Critic Loss: -7690.2207, W-Loss: -9629.4365, GP: 193.8833, Acc: 0.9232
Step  200/597 - Gen Loss: 2410.6033, Critic Loss: -11294.0537, W-Loss: -13858.0303, GP: 256.3205, Acc: 0.8892
Step  300/597 - Gen Loss: 4086.2915, Critic Loss: -16035.1670, W-Loss: -19430.1934, GP: 339.3728, Acc: 0.8651
Step  400/597 - Gen Loss: 6596.3428, Critic Loss: -23029.1172, W-Loss: -27382.4023, GP: 435.1331, Acc: 0.8531
Step  500/597 - Gen Loss: 10392.0439, Critic Loss: -32416.7539, W-Loss: -38219.3711, GP: 579.9567, Acc: 0.8136
----------------------------------------------------------------------

Epoch 3
------------------------------------------------------------
Step    0/597 - Gen Loss: 53448.3477, Critic Loss: -143933.1250, W-Loss: -153771.0938, GP: 982.2769, Acc: 0.2875
Step  100/597 - Gen Loss: 64274.5898, Critic Loss: -157272.9844, W-Loss: -177378.6406, GP: 2008.8613, Acc: 0.3061
Step  200/597 - Gen Loss: 80810.6094, Critic Loss: -192774.8906, W-Loss: -215255.9688, GP: 2245.9971, Acc: 0.2831
Step  300/597 - Gen Loss: 100183.0781, Critic Loss: -233923.0781, W-Loss: -260321.1250, GP: 2637.1680, Acc: 0.2577
Step  400/597 - Gen Loss: 115864.8047, Critic Loss: -179966.7031, W-Loss: -204393.4688, GP: 2439.7349, Acc: 0.2631
Step  500/597 - Gen Loss: 122804.7031, Critic Loss: -144031.6250, W-Loss: -163596.2500, GP: 1953.1084, Acc: 0.2638
----------------------------------------------------------------------

Epoch 4
------------------------------------------------------------
Step    0/597 - Gen Loss: 131942.4219, Critic Loss: 182.7888, W-Loss: 147.8906, GP: 0.1280, Acc: 0.3031
Step  100/597 - Gen Loss: 123666.1172, Critic Loss: 33.2206, W-Loss: -3.7786, GP: 0.1388, Acc: 0.2721
Step  200/597 - Gen Loss: 116928.7344, Critic Loss: 24.1903, W-Loss: -7.2244, GP: 0.1358, Acc: 0.3149
Step  300/597 - Gen Loss: 110796.9219, Critic Loss: 19.8649, W-Loss: -6.5400, GP: 0.1364, Acc: 0.4033
Step  400/597 - Gen Loss: 106547.2656, Critic Loss: 14.2029, W-Loss: -7.3097, GP: 0.1323, Acc: 0.5267
Step  500/597 - Gen Loss: 103389.3438, Critic Loss: 12.7054, W-Loss: -6.2687, GP: 0.1276, Acc: 0.5887
----------------------------------------------------------------------

Epoch 5
------------------------------------------------------------
Step    0/597 - Gen Loss: 82824.9219, Critic Loss: -71.1711, W-Loss: -72.0469, GP: 0.0876, Acc: 1.0000
Step  100/597 - Gen Loss: 81744.9062, Critic Loss: -12.6455, W-Loss: -16.7547, GP: 0.0964, Acc: 0.9294
Step  200/597 - Gen Loss: 81019.9766, Critic Loss: -8.1706, W-Loss: -12.7414, GP: 0.0929, Acc: 0.9121
Step  300/597 - Gen Loss: 79900.0547, Critic Loss: -5.4919, W-Loss: -9.8500, GP: 0.0916, Acc: 0.9175
Step  400/597 - Gen Loss: 78710.3203, Critic Loss: -2.9246, W-Loss: -6.9733, GP: 0.0906, Acc: 0.9229
Step  500/597 - Gen Loss: 77471.7500, Critic Loss: -1.0269, W-Loss: -5.0524, GP: 0.0892, Acc: 0.9196
----------------------------------------------------------------------

Epoch 6
------------------------------------------------------------
Step    0/597 - Gen Loss: 68433.4375, Critic Loss: -48.0617, W-Loss: -48.8937, GP: 0.0832, Acc: 1.0000
Step  100/597 - Gen Loss: 66745.4531, Critic Loss: 7.3389, W-Loss: 4.6892, GP: 0.0829, Acc: 0.9484
Step  200/597 - Gen Loss: 65809.2500, Critic Loss: -1.6725, W-Loss: -4.3446, GP: 0.0845, Acc: 0.9480
Step  300/597 - Gen Loss: 64655.0156, Critic Loss: 0.1449, W-Loss: -2.5453, GP: 0.0875, Acc: 0.9443
Step  400/597 - Gen Loss: 63561.3008, Critic Loss: -2.2382, W-Loss: -4.8428, GP: 0.0887, Acc: 0.9466
Step  500/597 - Gen Loss: 62480.7617, Critic Loss: -0.1807, W-Loss: -2.6742, GP: 0.0902, Acc: 0.9497
----------------------------------------------------------------------

Epoch 7
------------------------------------------------------------
Step    0/597 - Gen Loss: 55855.4375, Critic Loss: -224.6918, W-Loss: -225.5312, GP: 0.0839, Acc: 1.0000
Step  100/597 - Gen Loss: 54200.5195, Critic Loss: 3.3992, W-Loss: 1.0956, GP: 0.1026, Acc: 0.9485
Step  200/597 - Gen Loss: 53511.0156, Critic Loss: -1.1777, W-Loss: -3.2416, GP: 0.1070, Acc: 0.9592
Step  300/597 - Gen Loss: 52795.7852, Critic Loss: -4.7068, W-Loss: -6.7331, GP: 0.1120, Acc: 0.9621
Step  400/597 - Gen Loss: 52049.6914, Critic Loss: -4.2902, W-Loss: -6.3235, GP: 0.1158, Acc: 0.9632
Step  500/597 - Gen Loss: 51341.5391, Critic Loss: -5.3060, W-Loss: -7.3263, GP: 0.1184, Acc: 0.9644
----------------------------------------------------------------------

Epoch 8
------------------------------------------------------------
Step    0/597 - Gen Loss: 46082.0938, Critic Loss: -6.5016, W-Loss: -7.5836, GP: 0.1082, Acc: 1.0000
Step  100/597 - Gen Loss: 45453.9258, Critic Loss: -10.0887, W-Loss: -11.9534, GP: 0.1435, Acc: 0.9821
Step  200/597 - Gen Loss: 44821.9062, Critic Loss: -0.3133, W-Loss: -2.1979, GP: 0.1451, Acc: 0.9799
Step  300/597 - Gen Loss: 44156.4961, Critic Loss: -8.4548, W-Loss: -10.4012, GP: 0.1532, Acc: 0.9798
Step  400/597 - Gen Loss: 43582.7266, Critic Loss: -6.8840, W-Loss: -8.8439, GP: 0.1560, Acc: 0.9803
Step  500/597 - Gen Loss: 43056.7266, Critic Loss: -8.9574, W-Loss: -10.9321, GP: 0.1592, Acc: 0.9810
----------------------------------------------------------------------

Epoch 9
------------------------------------------------------------
Step    0/597 - Gen Loss: 39955.4141, Critic Loss: -221.9049, W-Loss: -233.0242, GP: 1.1119, Acc: 1.0000
Step  100/597 - Gen Loss: 39580.4180, Critic Loss: -12.5835, W-Loss: -22.4590, GP: 0.9659, Acc: 0.9878
Step  200/597 - Gen Loss: 39583.8242, Critic Loss: -35.3202, W-Loss: -50.0761, GP: 1.4553, Acc: 0.9877
Step  300/597 - Gen Loss: 39492.7070, Critic Loss: -43.3455, W-Loss: -63.4082, GP: 1.9867, Acc: 0.9873
Step  400/597 - Gen Loss: 39221.8945, Critic Loss: -49.8007, W-Loss: -74.6142, GP: 2.4626, Acc: 0.9878
Step  500/597 - Gen Loss: 38839.8398, Critic Loss: -51.4971, W-Loss: -81.2672, GP: 2.9592, Acc: 0.9878
----------------------------------------------------------------------

Epoch 10
------------------------------------------------------------
Step    0/597 - Gen Loss: 35983.1406, Critic Loss: -208.2044, W-Loss: -270.7008, GP: 6.2496, Acc: 1.0000
Step  100/597 - Gen Loss: 35526.7266, Critic Loss: -124.3812, W-Loss: -221.4867, GP: 9.6946, Acc: 0.9844
Step  200/597 - Gen Loss: 35474.6289, Critic Loss: -163.2366, W-Loss: -277.7753, GP: 11.4418, Acc: 0.9878
Step  300/597 - Gen Loss: 35592.3164, Critic Loss: -241.2654, W-Loss: -384.4381, GP: 14.3044, Acc: 0.9873
Step  400/597 - Gen Loss: 36026.5547, Critic Loss: -351.4949, W-Loss: -540.9835, GP: 18.9355, Acc: 0.9863
Step  500/597 - Gen Loss: 36686.5391, Critic Loss: -576.9902, W-Loss: -833.5413, GP: 25.6399, Acc: 0.9829
----------------------------------------------------------------------

Epoch 11
------------------------------------------------------------
Step    0/597 - Gen Loss: 40466.0078, Critic Loss: -3723.9055, W-Loss: -4217.5986, GP: 49.3637, Acc: 0.9844
Step  100/597 - Gen Loss: 40011.4570, Critic Loss: -2825.7065, W-Loss: -3896.4404, GP: 107.0596, Acc: 0.9802
Step  200/597 - Gen Loss: 41150.6992, Critic Loss: -3373.3899, W-Loss: -4626.2441, GP: 125.2759, Acc: 0.9857
Step  300/597 - Gen Loss: 42461.7930, Critic Loss: -3939.3069, W-Loss: -5420.0234, GP: 148.0629, Acc: 0.9873
Step  400/597 - Gen Loss: 43854.3203, Critic Loss: -4701.5430, W-Loss: -6465.3105, GP: 176.3694, Acc: 0.9891
Step  500/597 - Gen Loss: 44866.2891, Critic Loss: -5150.9688, W-Loss: -7156.8877, GP: 200.5853, Acc: 0.9899
----------------------------------------------------------------------

Epoch 12
------------------------------------------------------------
Step    0/597 - Gen Loss: 54691.8438, Critic Loss: -8829.5020, W-Loss: -12615.6104, GP: 378.6109, Acc: 1.0000
Step  100/597 - Gen Loss: 52560.1836, Critic Loss: -9675.3105, W-Loss: -14514.8125, GP: 483.9417, Acc: 0.9920
Step  200/597 - Gen Loss: 52307.4961, Critic Loss: -10896.4873, W-Loss: -15919.7705, GP: 502.3228, Acc: 0.9923
Step  300/597 - Gen Loss: 53303.7891, Critic Loss: -11906.7588, W-Loss: -17472.1152, GP: 556.5298, Acc: 0.9916
Step  400/597 - Gen Loss: 53943.9219, Critic Loss: -13383.7207, W-Loss: -19338.6133, GP: 595.4824, Acc: 0.9915
Step  500/597 - Gen Loss: 54947.5391, Critic Loss: -14196.6230, W-Loss: -20794.8320, GP: 659.8150, Acc: 0.9916
----------------------------------------------------------------------

Epoch 13
------------------------------------------------------------
Step    0/597 - Gen Loss: 64298.0273, Critic Loss: 169.6051, W-Loss: 61.1773, GP: 10.8428, Acc: 1.0000
Step  100/597 - Gen Loss: 61951.6602, Critic Loss: 65.4824, W-Loss: 41.7421, GP: 2.3638, Acc: 0.9944
Step  200/597 - Gen Loss: 60066.3867, Critic Loss: 28.6504, W-Loss: 15.5367, GP: 1.3062, Acc: 0.9972
Step  300/597 - Gen Loss: 58652.5430, Critic Loss: -6.6721, W-Loss: -15.8388, GP: 0.9132, Acc: 0.9981
Step  400/597 - Gen Loss: 57501.1484, Critic Loss: 3.5672, W-Loss: -3.4640, GP: 0.7006, Acc: 0.9986
Step  500/597 - Gen Loss: 56432.2930, Critic Loss: 0.8516, W-Loss: -4.8481, GP: 0.5679, Acc: 0.9989
----------------------------------------------------------------------

Epoch 14
------------------------------------------------------------
Step    0/597 - Gen Loss: 50530.6797, Critic Loss: 726.9702, W-Loss: 726.6813, GP: 0.0289, Acc: 1.0000
Step  100/597 - Gen Loss: 48884.4375, Critic Loss: 1.5188, W-Loss: 1.0236, GP: 0.0359, Acc: 0.9945
Step  200/597 - Gen Loss: 47393.9688, Critic Loss: 1.5815, W-Loss: 1.1915, GP: 0.0321, Acc: 0.9972
Step  300/597 - Gen Loss: 45935.0195, Critic Loss: -19.5610, W-Loss: -19.9265, GP: 0.0320, Acc: 0.9982
Step  400/597 - Gen Loss: 44486.7891, Critic Loss: -14.6572, W-Loss: -15.0170, GP: 0.0325, Acc: 0.9986
Step  500/597 - Gen Loss: 42967.6484, Critic Loss: -19.8738, W-Loss: -20.2370, GP: 0.0336, Acc: 0.9989
----------------------------------------------------------------------

Epoch 15
------------------------------------------------------------
Step    0/597 - Gen Loss: 32209.4844, Critic Loss: 201.9582, W-Loss: 201.5918, GP: 0.0366, Acc: 1.0000
Step  100/597 - Gen Loss: 31473.4844, Critic Loss: -11.6977, W-Loss: -14.3971, GP: 0.2518, Acc: 0.9929
Step  200/597 - Gen Loss: 31188.2344, Critic Loss: -5.6912, W-Loss: -8.8662, GP: 0.3084, Acc: 0.9965
Step  300/597 - Gen Loss: 30813.0273, Critic Loss: -24.1527, W-Loss: -29.3722, GP: 0.5099, Acc: 0.9949
Step  400/597 - Gen Loss: 30388.5762, Critic Loss: -17.5597, W-Loss: -26.1260, GP: 0.8423, Acc: 0.9938
Step  500/597 - Gen Loss: 29887.2422, Critic Loss: -20.7838, W-Loss: -34.2993, GP: 1.3368, Acc: 0.9933
----------------------------------------------------------------------

Epoch 16
------------------------------------------------------------
Step    0/597 - Gen Loss: 28278.0957, Critic Loss: 191.0257, W-Loss: 174.8680, GP: 1.6158, Acc: 1.0000
Step  100/597 - Gen Loss: 26865.5996, Critic Loss: -28.8465, W-Loss: -56.3607, GP: 2.7287, Acc: 0.9860
Step  200/597 - Gen Loss: 26421.2305, Critic Loss: -28.8572, W-Loss: -56.2524, GP: 2.7208, Acc: 0.9904
Step  300/597 - Gen Loss: 26248.5078, Critic Loss: -81.8056, W-Loss: -110.4927, GP: 2.8463, Acc: 0.9886
Step  400/597 - Gen Loss: 25958.9121, Critic Loss: -58.1378, W-Loss: -89.1676, GP: 3.0823, Acc: 0.9896
Step  500/597 - Gen Loss: 25527.1641, Critic Loss: -57.3645, W-Loss: -90.5654, GP: 3.2998, Acc: 0.9894
----------------------------------------------------------------------

Epoch 17
------------------------------------------------------------
Step    0/597 - Gen Loss: 22468.8379, Critic Loss: -974.5160, W-Loss: -1004.5875, GP: 3.0071, Acc: 1.0000
Step  100/597 - Gen Loss: 21297.4336, Critic Loss: 37.7225, W-Loss: -24.7019, GP: 6.2282, Acc: 0.9901
Step  200/597 - Gen Loss: 20398.5938, Critic Loss: -30.7132, W-Loss: -94.3902, GP: 6.3506, Acc: 0.9894
Step  300/597 - Gen Loss: 20012.5527, Critic Loss: -86.0829, W-Loss: -165.0127, GP: 7.8785, Acc: 0.9912
Step  400/597 - Gen Loss: 19383.4941, Critic Loss: -109.6275, W-Loss: -224.2629, GP: 11.4501, Acc: 0.9908
Step  500/597 - Gen Loss: 18766.6855, Critic Loss: -190.6171, W-Loss: -373.5858, GP: 18.2835, Acc: 0.9906
----------------------------------------------------------------------

Epoch 18
------------------------------------------------------------
Step    0/597 - Gen Loss: 12775.6738, Critic Loss: 218.7244, W-Loss: -2532.2234, GP: 275.0090, Acc: 0.9375
Step  100/597 - Gen Loss: 15653.8428, Critic Loss: -4771.5093, W-Loss: -5501.5503, GP: 72.9850, Acc: 0.9836
Step  200/597 - Gen Loss: 13932.1719, Critic Loss: -3249.5837, W-Loss: -3947.1013, GP: 69.7375, Acc: 0.9869
Step  300/597 - Gen Loss: 13386.9785, Critic Loss: -2572.1455, W-Loss: -3314.0012, GP: 74.1709, Acc: 0.9868
Step  400/597 - Gen Loss: 12961.7354, Critic Loss: -2203.0273, W-Loss: -2928.3164, GP: 72.5125, Acc: 0.9850
Step  500/597 - Gen Loss: 12599.8535, Critic Loss: -2160.9080, W-Loss: -2892.1387, GP: 73.1053, Acc: 0.9837
----------------------------------------------------------------------

Epoch 19
------------------------------------------------------------
Step    0/597 - Gen Loss: 10893.4043, Critic Loss: -1236.2723, W-Loss: -2041.9996, GP: 80.5727, Acc: 1.0000
Step  100/597 - Gen Loss: 8389.4385, Critic Loss: -2192.0007, W-Loss: -3095.6504, GP: 90.3539, Acc: 0.9869
Step  200/597 - Gen Loss: 8790.1279, Critic Loss: -2052.4526, W-Loss: -2840.9578, GP: 78.8397, Acc: 0.9864
Step  300/597 - Gen Loss: 9053.3643, Critic Loss: -2315.1902, W-Loss: -3195.2346, GP: 87.9935, Acc: 0.9865
Step  400/597 - Gen Loss: 9088.3594, Critic Loss: -2758.5647, W-Loss: -3653.7424, GP: 89.5072, Acc: 0.9861
Step  500/597 - Gen Loss: 9188.3438, Critic Loss: -2654.6758, W-Loss: -3598.1545, GP: 94.3380, Acc: 0.9871
----------------------------------------------------------------------

Epoch 20
------------------------------------------------------------
Step    0/597 - Gen Loss: 5328.5742, Critic Loss: -2334.5723, W-Loss: -2575.9062, GP: 24.1334, Acc: 1.0000
Step  100/597 - Gen Loss: 6199.2500, Critic Loss: -293.4164, W-Loss: -816.3134, GP: 52.2832, Acc: 0.9911
Step  200/597 - Gen Loss: 5148.7534, Critic Loss: -269.5768, W-Loss: -786.8328, GP: 51.7190, Acc: 0.9914
Step  300/597 - Gen Loss: 4591.6802, Critic Loss: -304.8382, W-Loss: -848.0193, GP: 54.3111, Acc: 0.9918
Step  400/597 - Gen Loss: 3988.5623, Critic Loss: -307.8248, W-Loss: -902.1447, GP: 59.4255, Acc: 0.9922
Step  500/597 - Gen Loss: 3752.5276, Critic Loss: -385.8986, W-Loss: -1011.6339, GP: 62.5672, Acc: 0.9922
----------------------------------------------------------------------

Epoch 21
------------------------------------------------------------
Step    0/597 - Gen Loss: 1439.5823, Critic Loss: -1402.5095, W-Loss: -2405.4614, GP: 100.2501, Acc: 0.9094
Step  100/597 - Gen Loss: -722.8683, Critic Loss: -925.8400, W-Loss: -2138.1533, GP: 121.2275, Acc: 0.9933
Step  200/597 - Gen Loss: -911.6037, Critic Loss: -987.7186, W-Loss: -2128.6487, GP: 114.0891, Acc: 0.9939
Step  300/597 - Gen Loss: -1446.6743, Critic Loss: -1164.7976, W-Loss: -2256.2881, GP: 109.1444, Acc: 0.9933
Step  400/597 - Gen Loss: -1763.0546, Critic Loss: -1420.9772, W-Loss: -2533.4402, GP: 111.2415, Acc: 0.9936
Step  500/597 - Gen Loss: -1744.3406, Critic Loss: -1673.1755, W-Loss: -2826.5208, GP: 115.3303, Acc: 0.9944
----------------------------------------------------------------------

Epoch 22
------------------------------------------------------------
Step    0/597 - Gen Loss: 1823.4227, Critic Loss: -5201.3643, W-Loss: -6009.9463, GP: 80.8582, Acc: 1.0000
Step  100/597 - Gen Loss: -1987.2332, Critic Loss: -4373.2886, W-Loss: -6122.8281, GP: 174.9476, Acc: 0.9929
Step  200/597 - Gen Loss: -2070.6851, Critic Loss: -4621.4448, W-Loss: -6519.8525, GP: 189.8357, Acc: 0.9930
Step  300/597 - Gen Loss: -1436.7054, Critic Loss: -4857.4111, W-Loss: -6898.3184, GP: 204.0865, Acc: 0.9933
Step  400/597 - Gen Loss: -685.1618, Critic Loss: -5368.0068, W-Loss: -7570.5142, GP: 220.2467, Acc: 0.9935
Step  500/597 - Gen Loss: -16.6916, Critic Loss: -5378.2769, W-Loss: -7705.0688, GP: 232.6748, Acc: 0.9939
----------------------------------------------------------------------

Epoch 23
------------------------------------------------------------
Step    0/597 - Gen Loss: 4238.7344, Critic Loss: -12920.6396, W-Loss: -15076.1768, GP: 215.5537, Acc: 1.0000
Step  100/597 - Gen Loss: 4267.5850, Critic Loss: -10146.2529, W-Loss: -14186.6455, GP: 404.0372, Acc: 0.9970
Step  200/597 - Gen Loss: 5342.1167, Critic Loss: -10810.9219, W-Loss: -15075.5078, GP: 426.4557, Acc: 0.9950
Step  300/597 - Gen Loss: 5844.2065, Critic Loss: -10984.9346, W-Loss: -15607.9766, GP: 462.3016, Acc: 0.9953
Step  400/597 - Gen Loss: 7355.7334, Critic Loss: -12110.5371, W-Loss: -16949.5762, GP: 483.9005, Acc: 0.9951
Step  500/597 - Gen Loss: 8642.3789, Critic Loss: -13247.9766, W-Loss: -18416.4023, GP: 516.8376, Acc: 0.9949
----------------------------------------------------------------------

Epoch 24
------------------------------------------------------------
Step    0/597 - Gen Loss: 25681.4609, Critic Loss: -17869.1953, W-Loss: -22885.0273, GP: 501.5831, Acc: 1.0000
Step  100/597 - Gen Loss: 17861.1836, Critic Loss: -20373.1055, W-Loss: -28777.7090, GP: 840.4575, Acc: 0.9933
Step  200/597 - Gen Loss: 18179.0527, Critic Loss: -21859.8340, W-Loss: -30401.9922, GP: 854.2130, Acc: 0.9937
Step  300/597 - Gen Loss: 19209.4277, Critic Loss: -22978.5938, W-Loss: -32157.2598, GP: 917.8622, Acc: 0.9937
Step  400/597 - Gen Loss: 20591.2129, Critic Loss: -23698.5547, W-Loss: -33263.5273, GP: 956.4903, Acc: 0.9933
Step  500/597 - Gen Loss: 22367.2148, Critic Loss: -24556.3730, W-Loss: -34453.6016, GP: 989.7211, Acc: 0.9935
----------------------------------------------------------------------

Epoch 25
------------------------------------------------------------
Step    0/597 - Gen Loss: 38544.0977, Critic Loss: -24859.8203, W-Loss: -33337.7188, GP: 847.7897, Acc: 1.0000
Step  100/597 - Gen Loss: 37097.3359, Critic Loss: -30862.3203, W-Loss: -45747.5742, GP: 1488.5193, Acc: 0.9920
Step  200/597 - Gen Loss: 36067.7266, Critic Loss: -31835.2051, W-Loss: -47893.0742, GP: 1605.7770, Acc: 0.9913
Step  300/597 - Gen Loss: 38435.6094, Critic Loss: -33940.5312, W-Loss: -50297.2031, GP: 1635.6628, Acc: 0.9918
Step  400/597 - Gen Loss: 39918.9727, Critic Loss: -35243.4023, W-Loss: -51805.7695, GP: 1656.2356, Acc: 0.9925
Step  500/597 - Gen Loss: 41167.9219, Critic Loss: -36024.9414, W-Loss: -52895.0469, GP: 1687.0090, Acc: 0.9921
----------------------------------------------------------------------

Epoch 26
------------------------------------------------------------
Step    0/597 - Gen Loss: 57489.2852, Critic Loss: -72320.6250, W-Loss: -90305.8984, GP: 1798.5277, Acc: 1.0000
Step  100/597 - Gen Loss: 62676.2773, Critic Loss: -1255.0797, W-Loss: -4891.7769, GP: 363.6695, Acc: 1.0000
Step  200/597 - Gen Loss: 60726.1641, Critic Loss: -558.1940, W-Loss: -2394.0767, GP: 183.5880, Acc: 1.0000
Step  300/597 - Gen Loss: 59460.6172, Critic Loss: -463.0557, W-Loss: -1689.8739, GP: 122.6815, Acc: 1.0000
Step  400/597 - Gen Loss: 58524.0117, Critic Loss: -311.6929, W-Loss: -1232.8915, GP: 92.1196, Acc: 1.0000
Step  500/597 - Gen Loss: 57921.3398, Critic Loss: -302.5737, W-Loss: -1040.0468, GP: 73.7471, Acc: 1.0000
----------------------------------------------------------------------

Epoch 27
------------------------------------------------------------
Step    0/597 - Gen Loss: 59651.8555, Critic Loss: 561.2914, W-Loss: 560.5961, GP: 0.0695, Acc: 1.0000
Step  100/597 - Gen Loss: 54376.5195, Critic Loss: 157.3714, W-Loss: 156.8650, GP: 0.0506, Acc: 1.0000
Step  200/597 - Gen Loss: 53593.8477, Critic Loss: 148.1006, W-Loss: 147.6147, GP: 0.0486, Acc: 1.0000
Step  300/597 - Gen Loss: 53376.0625, Critic Loss: -6.7116, W-Loss: -7.1981, GP: 0.0486, Acc: 1.0000
Step  400/597 - Gen Loss: 52497.7148, Critic Loss: -21.2191, W-Loss: -21.7121, GP: 0.0493, Acc: 1.0000
Step  500/597 - Gen Loss: 51818.5898, Critic Loss: -101.7914, W-Loss: -102.2968, GP: 0.0505, Acc: 1.0000
----------------------------------------------------------------------

Epoch 28
------------------------------------------------------------
Step    0/597 - Gen Loss: 47610.8516, Critic Loss: -5891.5884, W-Loss: -5893.7969, GP: 0.2209, Acc: 1.0000
Step  100/597 - Gen Loss: 46027.9688, Critic Loss: 126.4455, W-Loss: 118.5152, GP: 0.7931, Acc: 1.0000
Step  200/597 - Gen Loss: 45523.9219, Critic Loss: -8.2591, W-Loss: -18.7424, GP: 1.0483, Acc: 1.0000
Step  300/597 - Gen Loss: 44623.9805, Critic Loss: -206.9651, W-Loss: -218.8379, GP: 1.1873, Acc: 1.0000
Step  400/597 - Gen Loss: 43987.5469, Critic Loss: -170.4484, W-Loss: -182.7485, GP: 1.2300, Acc: 1.0000
Step  500/597 - Gen Loss: 43607.7969, Critic Loss: -185.6228, W-Loss: -198.4662, GP: 1.2843, Acc: 1.0000
----------------------------------------------------------------------

Epoch 29
------------------------------------------------------------
Step    0/597 - Gen Loss: 40308.5312, Critic Loss: 685.1334, W-Loss: 659.6180, GP: 2.5515, Acc: 1.0000
Step  100/597 - Gen Loss: 39903.6328, Critic Loss: -306.0666, W-Loss: -321.8192, GP: 1.5753, Acc: 1.0000
Step  200/597 - Gen Loss: 38819.3320, Critic Loss: -183.3323, W-Loss: -198.0027, GP: 1.4671, Acc: 1.0000
Step  300/597 - Gen Loss: 37637.7500, Critic Loss: -357.0096, W-Loss: -371.3998, GP: 1.4390, Acc: 1.0000
Step  400/597 - Gen Loss: 36709.6250, Critic Loss: -204.5079, W-Loss: -219.2679, GP: 1.4760, Acc: 1.0000
Step  500/597 - Gen Loss: 35890.8242, Critic Loss: -283.3685, W-Loss: -298.3405, GP: 1.4972, Acc: 1.0000
----------------------------------------------------------------------

Epoch 30
------------------------------------------------------------
Step    0/597 - Gen Loss: 33856.6484, Critic Loss: 1762.5551, W-Loss: 1754.3011, GP: 0.8254, Acc: 1.0000
Step  100/597 - Gen Loss: 31335.6660, Critic Loss: 143.4762, W-Loss: 127.0455, GP: 1.6431, Acc: 1.0000
Step  200/597 - Gen Loss: 30253.5176, Critic Loss: 152.3405, W-Loss: 135.6001, GP: 1.6740, Acc: 1.0000
Step  300/597 - Gen Loss: 29790.4746, Critic Loss: -86.7352, W-Loss: -103.9012, GP: 1.7166, Acc: 1.0000
Step  400/597 - Gen Loss: 29367.7598, Critic Loss: 44.7897, W-Loss: 26.3045, GP: 1.8485, Acc: 1.0000
Step  500/597 - Gen Loss: 28728.3613, Critic Loss: -86.9621, W-Loss: -110.1171, GP: 2.3155, Acc: 1.0000
----------------------------------------------------------------------

Epoch 31
------------------------------------------------------------
Step    0/597 - Gen Loss: 26127.7070, Critic Loss: -5950.2651, W-Loss: -6005.5122, GP: 5.5247, Acc: 1.0000
Step  100/597 - Gen Loss: 27159.8691, Critic Loss: 323.3791, W-Loss: 296.5315, GP: 2.6847, Acc: 1.0000
Step  200/597 - Gen Loss: 27618.0156, Critic Loss: 190.8043, W-Loss: 162.9373, GP: 2.7867, Acc: 1.0000
Step  300/597 - Gen Loss: 27527.9766, Critic Loss: -164.2307, W-Loss: -194.2526, GP: 3.0022, Acc: 1.0000
Step  400/597 - Gen Loss: 27699.1094, Critic Loss: -148.7848, W-Loss: -182.4206, GP: 3.3636, Acc: 1.0000
Step  500/597 - Gen Loss: 27919.8184, Critic Loss: -135.9250, W-Loss: -174.2653, GP: 3.8340, Acc: 1.0000
----------------------------------------------------------------------

Epoch 32
------------------------------------------------------------
Step    0/597 - Gen Loss: 24876.6660, Critic Loss: 2397.8525, W-Loss: 2366.2551, GP: 3.1598, Acc: 1.0000
Step  100/597 - Gen Loss: 29160.4082, Critic Loss: 24.1367, W-Loss: -63.1435, GP: 8.7280, Acc: 1.0000
Step  200/597 - Gen Loss: 28443.8555, Critic Loss: 7.1769, W-Loss: -97.2432, GP: 10.4420, Acc: 1.0000
Step  300/597 - Gen Loss: 27937.0273, Critic Loss: -274.3630, W-Loss: -396.2932, GP: 12.1930, Acc: 1.0000
Step  400/597 - Gen Loss: 27293.8770, Critic Loss: -237.1922, W-Loss: -392.4036, GP: 15.5211, Acc: 1.0000
Step  500/597 - Gen Loss: 26500.9453, Critic Loss: -318.6702, W-Loss: -503.9710, GP: 18.5301, Acc: 1.0000
----------------------------------------------------------------------

Epoch 33
------------------------------------------------------------
Step    0/597 - Gen Loss: 21693.7109, Critic Loss: 1206.3599, W-Loss: 913.6668, GP: 29.2693, Acc: 1.0000
Step  100/597 - Gen Loss: 24410.6465, Critic Loss: -1565.3433, W-Loss: -2054.5747, GP: 48.9232, Acc: 1.0000
Step  200/597 - Gen Loss: 24390.7715, Critic Loss: -1206.4850, W-Loss: -1790.1974, GP: 58.3712, Acc: 1.0000
Step  300/597 - Gen Loss: 24225.2793, Critic Loss: -1808.0894, W-Loss: -2474.1719, GP: 66.6083, Acc: 1.0000
Step  400/597 - Gen Loss: 24412.5312, Critic Loss: -2001.7546, W-Loss: -2762.7766, GP: 76.1022, Acc: 1.0000
Step  500/597 - Gen Loss: 24558.8711, Critic Loss: -2381.6116, W-Loss: -3253.9851, GP: 87.2373, Acc: 0.9997
----------------------------------------------------------------------

Epoch 34
------------------------------------------------------------
Step    0/597 - Gen Loss: 36473.6875, Critic Loss: -17427.4180, W-Loss: -19515.4648, GP: 208.8044, Acc: 1.0000
Step  100/597 - Gen Loss: 28056.5352, Critic Loss: -5476.2935, W-Loss: -7471.8550, GP: 199.5555, Acc: 0.9979
Step  200/597 - Gen Loss: 27658.0449, Critic Loss: -5705.5215, W-Loss: -7883.3579, GP: 217.7822, Acc: 0.9970
Step  300/597 - Gen Loss: 27410.7441, Critic Loss: -6782.8496, W-Loss: -9253.1709, GP: 247.0307, Acc: 0.9971
Step  400/597 - Gen Loss: 27801.1914, Critic Loss: -7681.1489, W-Loss: -10427.3389, GP: 274.6183, Acc: 0.9973
Step  500/597 - Gen Loss: 28421.2578, Critic Loss: -8768.3037, W-Loss: -11817.9893, GP: 304.9685, Acc: 0.9972
----------------------------------------------------------------------

Epoch 35
------------------------------------------------------------
Step    0/597 - Gen Loss: 38431.9102, Critic Loss: -12722.0225, W-Loss: -16359.8779, GP: 363.7855, Acc: 1.0000
Step  100/597 - Gen Loss: 36308.4648, Critic Loss: -16176.4248, W-Loss: -22265.4082, GP: 608.8958, Acc: 0.9953
Step  200/597 - Gen Loss: 36513.7070, Critic Loss: -17433.9902, W-Loss: -23877.7324, GP: 644.3700, Acc: 0.9940
Step  300/597 - Gen Loss: 38000.5625, Critic Loss: -19402.6309, W-Loss: -26355.3145, GP: 695.2634, Acc: 0.9942
Step  400/597 - Gen Loss: 39684.1016, Critic Loss: -20586.8730, W-Loss: -28122.0645, GP: 753.5169, Acc: 0.9940
Step  500/597 - Gen Loss: 41191.8164, Critic Loss: -22222.6562, W-Loss: -30260.1777, GP: 803.7516, Acc: 0.9937
----------------------------------------------------------------------

Epoch 36
------------------------------------------------------------
Step    0/597 - Gen Loss: 59596.5273, Critic Loss: -50369.6992, W-Loss: -56933.7891, GP: 656.4088, Acc: 1.0000
Step  100/597 - Gen Loss: 57983.4219, Critic Loss: -35269.5156, W-Loss: -48090.9453, GP: 1282.1381, Acc: 0.9930
Step  200/597 - Gen Loss: 56740.6875, Critic Loss: -36523.5195, W-Loss: -49765.2891, GP: 1324.1738, Acc: 0.9922
Step  300/597 - Gen Loss: 56805.8281, Critic Loss: -38436.0352, W-Loss: -52526.6172, GP: 1409.0516, Acc: 0.9911
Step  400/597 - Gen Loss: 57662.1133, Critic Loss: -39191.2422, W-Loss: -54169.7852, GP: 1497.8552, Acc: 0.9916
Step  500/597 - Gen Loss: 57931.6094, Critic Loss: -40688.9883, W-Loss: -56495.5352, GP: 1580.6555, Acc: 0.9914
----------------------------------------------------------------------

Epoch 37
------------------------------------------------------------
Step    0/597 - Gen Loss: 76846.4062, Critic Loss: -71816.3594, W-Loss: -99746.4688, GP: 2793.0090, Acc: 0.9875
Step  100/597 - Gen Loss: 65899.3359, Critic Loss: -57365.7656, W-Loss: -78931.8828, GP: 2156.6045, Acc: 0.9912
Step  200/597 - Gen Loss: 65778.6406, Critic Loss: -57096.0195, W-Loss: -80175.5781, GP: 2307.9446, Acc: 0.9875
Step  300/597 - Gen Loss: 66998.5078, Critic Loss: -59456.3281, W-Loss: -83368.3906, GP: 2391.1902, Acc: 0.9875
Step  400/597 - Gen Loss: 68231.8125, Critic Loss: -61567.6328, W-Loss: -86501.4453, GP: 2493.3669, Acc: 0.9876
Step  500/597 - Gen Loss: 69948.2266, Critic Loss: -63742.2070, W-Loss: -89553.1016, GP: 2581.0754, Acc: 0.9872
----------------------------------------------------------------------

Epoch 38
------------------------------------------------------------
Step    0/597 - Gen Loss: 88108.7500, Critic Loss: -56261.9141, W-Loss: -99611.2266, GP: 4334.9307, Acc: 1.0000
Step  100/597 - Gen Loss: 87709.2188, Critic Loss: -81443.7656, W-Loss: -114317.6406, GP: 3287.3647, Acc: 0.9820
Step  200/597 - Gen Loss: 89172.6094, Critic Loss: -78768.1328, W-Loss: -112814.4141, GP: 3404.6072, Acc: 0.9815
Step  300/597 - Gen Loss: 90251.1641, Critic Loss: -81779.5469, W-Loss: -117048.8750, GP: 3526.9097, Acc: 0.9825
Step  400/597 - Gen Loss: 92664.5469, Critic Loss: -84686.3984, W-Loss: -121328.4531, GP: 3664.1907, Acc: 0.9825
Step  500/597 - Gen Loss: 94354.0312, Critic Loss: -85034.3516, W-Loss: -124298.4844, GP: 3926.3948, Acc: 0.9813
----------------------------------------------------------------------

Epoch 39
------------------------------------------------------------
Step    0/597 - Gen Loss: 128216.2344, Critic Loss: -114830.1875, W-Loss: -163178.0312, GP: 4834.7837, Acc: 1.0000
Step  100/597 - Gen Loss: 108501.2500, Critic Loss: -104055.2734, W-Loss: -152033.8438, GP: 4797.8398, Acc: 0.9764
Step  200/597 - Gen Loss: 109451.2500, Critic Loss: -107102.5547, W-Loss: -153764.8906, GP: 4666.2354, Acc: 0.9745
Step  300/597 - Gen Loss: 109346.3594, Critic Loss: -108112.8516, W-Loss: -156077.2656, GP: 4796.4434, Acc: 0.9746
Step  400/597 - Gen Loss: 113058.5859, Critic Loss: -110806.8906, W-Loss: -160282.4062, GP: 4947.5317, Acc: 0.9745
Step  500/597 - Gen Loss: 114125.3750, Critic Loss: -113617.8984, W-Loss: -165088.9531, GP: 5147.0830, Acc: 0.9745
----------------------------------------------------------------------

Epoch 40
------------------------------------------------------------
Step    0/597 - Gen Loss: 91336.1719, Critic Loss: -144873.3438, W-Loss: -170070.8750, GP: 2519.7515, Acc: 0.9969
Step  100/597 - Gen Loss: 135386.7188, Critic Loss: -135688.9375, W-Loss: -193215.5938, GP: 5752.6387, Acc: 0.9732
Step  200/597 - Gen Loss: 133538.2812, Critic Loss: -132227.4375, W-Loss: -191792.5469, GP: 5956.4858, Acc: 0.9723
Step  300/597 - Gen Loss: 131794.4375, Critic Loss: -135131.9531, W-Loss: -196181.1562, GP: 6104.8936, Acc: 0.9693
Step  400/597 - Gen Loss: 133300.3125, Critic Loss: -139235.1406, W-Loss: -201956.9062, GP: 6272.1543, Acc: 0.9677
Step  500/597 - Gen Loss: 136215.8438, Critic Loss: -141393.1406, W-Loss: -204851.4688, GP: 6345.8101, Acc: 0.9685
----------------------------------------------------------------------

Epoch 41
------------------------------------------------------------
Step    0/597 - Gen Loss: 177035.2812, Critic Loss: -186570.0938, W-Loss: -234745.0000, GP: 4817.4878, Acc: 0.9969
Step  100/597 - Gen Loss: 153041.7188, Critic Loss: -174835.1719, W-Loss: -246481.2031, GP: 7164.5713, Acc: 0.9620
Step  200/597 - Gen Loss: 152182.9375, Critic Loss: -169685.5000, W-Loss: -243068.1094, GP: 7338.2251, Acc: 0.9606
Step  300/597 - Gen Loss: 155302.0469, Critic Loss: -171750.0156, W-Loss: -248068.3125, GP: 7631.7754, Acc: 0.9595
Step  400/597 - Gen Loss: 158108.0781, Critic Loss: -175000.5625, W-Loss: -252996.8750, GP: 7799.5723, Acc: 0.9601
Step  500/597 - Gen Loss: 159627.2188, Critic Loss: -179533.0000, W-Loss: -258743.3906, GP: 7921.0117, Acc: 0.9593
----------------------------------------------------------------------

Epoch 42
------------------------------------------------------------
Step    0/597 - Gen Loss: 204975.1562, Critic Loss: -243908.4531, W-Loss: -338831.0938, GP: 9492.2646, Acc: 1.0000
Step  100/597 - Gen Loss: 169561.0625, Critic Loss: -205284.5156, W-Loss: -301748.2500, GP: 9646.3193, Acc: 0.9523
Step  200/597 - Gen Loss: 180741.0781, Critic Loss: -195415.3594, W-Loss: -298775.5312, GP: 10335.9570, Acc: 0.9491
Step  300/597 - Gen Loss: 174885.2500, Critic Loss: -196649.2656, W-Loss: -299352.2812, GP: 10270.2344, Acc: 0.9507
Step  400/597 - Gen Loss: 183406.1406, Critic Loss: -192257.0781, W-Loss: -293000.7812, GP: 10074.3076, Acc: 0.9500
Step  500/597 - Gen Loss: 184230.5156, Critic Loss: -198819.1406, W-Loss: -299818.9062, GP: 10099.8848, Acc: 0.9511
----------------------------------------------------------------------

Epoch 43
------------------------------------------------------------
Step    0/597 - Gen Loss: 258228.2656, Critic Loss: -123265.3594, W-Loss: -191752.9531, GP: 6848.7578, Acc: 1.0000
Step  100/597 - Gen Loss: 218830.7188, Critic Loss: -235074.0156, W-Loss: -342120.0312, GP: 10704.5205, Acc: 0.9384
Step  200/597 - Gen Loss: 218636.7031, Critic Loss: -224810.2344, W-Loss: -333354.0000, GP: 10854.2969, Acc: 0.9364
Step  300/597 - Gen Loss: 216499.0625, Critic Loss: -233624.1719, W-Loss: -344717.6250, GP: 11109.2363, Acc: 0.9365
Step  400/597 - Gen Loss: 216744.7812, Critic Loss: -240096.6406, W-Loss: -354028.4062, GP: 11393.0908, Acc: 0.9390
Step  500/597 - Gen Loss: 217046.2188, Critic Loss: -247114.1250, W-Loss: -361560.2500, GP: 11444.5342, Acc: 0.9399
----------------------------------------------------------------------

Epoch 44
------------------------------------------------------------
Step    0/597 - Gen Loss: 318626.5625, Critic Loss: -326193.7500, W-Loss: -428349.6875, GP: 10215.5986, Acc: 1.0000
Step  100/597 - Gen Loss: 235246.2500, Critic Loss: -273175.7188, W-Loss: -400616.8750, GP: 12743.9893, Acc: 0.9237
Step  200/597 - Gen Loss: 246604.5000, Critic Loss: -277244.9688, W-Loss: -405858.6562, GP: 12861.2461, Acc: 0.9221
Step  300/597 - Gen Loss: 249228.3594, Critic Loss: -281038.9375, W-Loss: -411449.1562, GP: 13040.8613, Acc: 0.9254
Step  400/597 - Gen Loss: 253020.2969, Critic Loss: -288480.4375, W-Loss: -420768.0312, GP: 13228.5615, Acc: 0.9239
Step  500/597 - Gen Loss: 256735.2344, Critic Loss: -284960.1562, W-Loss: -418468.8750, GP: 13350.7129, Acc: 0.9248
----------------------------------------------------------------------

Epoch 45
------------------------------------------------------------
Step    0/597 - Gen Loss: 334633.6875, Critic Loss: -365433.6562, W-Loss: -483013.0938, GP: 11757.9473, Acc: 1.0000
Step  100/597 - Gen Loss: 278770.3438, Critic Loss: -337903.5312, W-Loss: -483829.8438, GP: 14592.4287, Acc: 0.9099
Step  200/597 - Gen Loss: 275008.7500, Critic Loss: -311325.7188, W-Loss: -465231.9062, GP: 15390.4824, Acc: 0.9106
Step  300/597 - Gen Loss: 278685.0625, Critic Loss: -319268.3438, W-Loss: -474086.7500, GP: 15481.6729, Acc: 0.9143
Step  400/597 - Gen Loss: 285197.5312, Critic Loss: -324913.0625, W-Loss: -482599.7188, GP: 15768.4980, Acc: 0.9149
Step  500/597 - Gen Loss: 295563.3750, Critic Loss: -320383.1562, W-Loss: -479232.0625, GP: 15884.8027, Acc: 0.9159
----------------------------------------------------------------------

Epoch 46
------------------------------------------------------------
Step    0/597 - Gen Loss: 397643.0000, Critic Loss: -201566.0469, W-Loss: -445644.0938, GP: 24407.8008, Acc: 1.0000
Step  100/597 - Gen Loss: 322040.2500, Critic Loss: -366610.9375, W-Loss: -531245.3125, GP: 16463.2363, Acc: 0.9209
Step  200/597 - Gen Loss: 325780.5000, Critic Loss: -362135.1875, W-Loss: -527551.0625, GP: 16541.4609, Acc: 0.9289
Step  300/597 - Gen Loss: 322088.2500, Critic Loss: -360161.2188, W-Loss: -532142.5000, GP: 17197.9980, Acc: 0.9241
Step  400/597 - Gen Loss: 325232.0000, Critic Loss: -341496.0625, W-Loss: -516075.0938, GP: 17457.7891, Acc: 0.9153
Step  500/597 - Gen Loss: 328065.2188, Critic Loss: -344858.5000, W-Loss: -520722.2500, GP: 17586.3535, Acc: 0.9145
----------------------------------------------------------------------

Epoch 47
------------------------------------------------------------
Step    0/597 - Gen Loss: 463389.9688, Critic Loss: -485138.0625, W-Loss: -690172.5625, GP: 20503.4492, Acc: 0.9969
Step  100/597 - Gen Loss: 364716.8438, Critic Loss: -392012.8438, W-Loss: -584716.5000, GP: 19270.1602, Acc: 0.9014
Step  200/597 - Gen Loss: 370739.8750, Critic Loss: -353929.0938, W-Loss: -548226.5625, GP: 19429.5469, Acc: 0.9044
Step  300/597 - Gen Loss: 372818.2188, Critic Loss: -344278.6562, W-Loss: -533203.5000, GP: 18892.2422, Acc: 0.9010
Step  400/597 - Gen Loss: 372757.2812, Critic Loss: -350329.3438, W-Loss: -539887.5625, GP: 18955.5703, Acc: 0.9003
Step  500/597 - Gen Loss: 372480.3438, Critic Loss: -366465.1875, W-Loss: -557466.3125, GP: 19099.8164, Acc: 0.8990
----------------------------------------------------------------------

Epoch 48
------------------------------------------------------------
Step    0/597 - Gen Loss: 506033.6875, Critic Loss: -478419.0000, W-Loss: -681212.4375, GP: 20279.3418, Acc: 1.0000
Step  100/597 - Gen Loss: 396205.6250, Critic Loss: -457952.5625, W-Loss: -657486.7500, GP: 19953.1855, Acc: 0.8987
Step  200/597 - Gen Loss: 394974.2500, Critic Loss: -450017.1562, W-Loss: -655358.2500, GP: 20533.8047, Acc: 0.8957
Step  300/597 - Gen Loss: 389430.6562, Critic Loss: -439038.4688, W-Loss: -649781.6875, GP: 21074.0605, Acc: 0.8904
Step  400/597 - Gen Loss: 391188.9062, Critic Loss: -446317.0312, W-Loss: -659210.9375, GP: 21289.1445, Acc: 0.8937
Step  500/597 - Gen Loss: 394686.1250, Critic Loss: -447099.4062, W-Loss: -664240.8750, GP: 21713.8711, Acc: 0.8938
----------------------------------------------------------------------

Epoch 49
------------------------------------------------------------
Step    0/597 - Gen Loss: 439782.6562, Critic Loss: -784534.9375, W-Loss: -889073.5000, GP: 10453.8564, Acc: 1.0000
Step  100/597 - Gen Loss: 404360.7812, Critic Loss: -484866.6250, W-Loss: -714204.1875, GP: 22933.4355, Acc: 0.8826
Step  200/597 - Gen Loss: 417858.7500, Critic Loss: -475573.9688, W-Loss: -709044.5625, GP: 23346.7363, Acc: 0.8843
Step  300/597 - Gen Loss: 426702.8438, Critic Loss: -483148.3750, W-Loss: -721536.0625, GP: 23838.2930, Acc: 0.8815
Step  400/597 - Gen Loss: 430043.1875, Critic Loss: -490235.1875, W-Loss: -730323.5625, GP: 24008.3867, Acc: 0.8821
Step  500/597 - Gen Loss: 436194.1250, Critic Loss: -466769.0000, W-Loss: -710459.3125, GP: 24368.5645, Acc: 0.8759
----------------------------------------------------------------------

Epoch 50
------------------------------------------------------------
Step    0/597 - Gen Loss: 343802.8125, Critic Loss: -652384.7500, W-Loss: -879279.6250, GP: 22689.4805, Acc: 1.0000
Step  100/597 - Gen Loss: 446474.4688, Critic Loss: -555206.5000, W-Loss: -793300.2500, GP: 23809.0137, Acc: 0.8645
Step  200/597 - Gen Loss: 450908.2500, Critic Loss: -539709.6250, W-Loss: -790906.6250, GP: 25119.2012, Acc: 0.8574
Step  300/597 - Gen Loss: 452102.8125, Critic Loss: -543916.8125, W-Loss: -801672.2500, GP: 25775.0645, Acc: 0.8553
Step  400/597 - Gen Loss: 461958.5000, Critic Loss: -540601.6250, W-Loss: -801479.6250, GP: 26087.2754, Acc: 0.8594
Step  500/597 - Gen Loss: 473245.2500, Critic Loss: -533154.6250, W-Loss: -796866.5000, GP: 26370.7090, Acc: 0.8593

Generating Grid of Samples at Final Epoch 50:
Generating 6 samples per class for all 16 letter classes...

----------------------------------------------------------------------

Final Generated Samples - All Letter Classes:
Generating 6 samples per class for all 16 letter classes...

# =============================================================================
# ENHANCED WGAN TRAINING - 50 EPOCHS
# =============================================================================

# Test visualization first
test_images_enhanced, test_labels_enhanced = display_generated_samples_grid(
    enhanced_wgan_generator, class_to_letter, samples_per_class=6
)

# Training configuration
NUM_EPOCHS = 50

start_time = time.time()

for epoch in range(NUM_EPOCHS):
    epoch_start = time.time()
    
    # Train for one epoch with WGAN-GP strategy
    enhanced_wgan_trainer_new.train_epoch(train_dataset, epoch, steps_per_epoch)
    
    # Display only on the final epoch
    if (epoch + 1) == NUM_EPOCHS:
        print(f"\nGenerating Grid of Samples at Final Epoch {epoch + 1}:")
        display_generated_samples_grid(enhanced_wgan_generator, class_to_letter, epoch + 1, samples_per_class=6)
    
    # Calculate and display epoch timing
    epoch_time = time.time() - epoch_start
    total_time = time.time() - start_time
    avg_time = total_time / (epoch + 1)
    eta = avg_time * (NUM_EPOCHS - epoch - 1)

total_training_time = time.time() - start_time

# Generate final display
print(f"\nFinal Generated Samples - All Letter Classes:")
final_images_enhanced, final_labels_enhanced = display_generated_samples_grid(
    enhanced_wgan_generator, class_to_letter, NUM_EPOCHS, samples_per_class=6
)

# Plot training progress
if len(enhanced_wgan_trainer_new.history['gen_loss']) > 1:
    plt.figure(figsize=(15, 5))
    
    # Generator and Critic Loss
    plt.subplot(1, 3, 1)
    epochs = enhanced_wgan_trainer_new.history['epoch']
    plt.plot(epochs, enhanced_wgan_trainer_new.history['gen_loss'], label='Generator Loss', color='blue', linewidth=2)
    plt.plot(epochs, enhanced_wgan_trainer_new.history['critic_loss'], label='Critic Loss', color='red', linewidth=2)
    plt.title('Enhanced WGAN - Training Losses', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Critic Accuracy
    plt.subplot(1, 3, 2)
    plt.plot(epochs, enhanced_wgan_trainer_new.history['label_accuracy'], label='Critic Accuracy', color='green', linewidth=2)
    plt.axhline(y=0.95, color='red', linestyle='--', alpha=0.7, label='Upper limit (95%)')
    plt.axhline(y=0.70, color='orange', linestyle='--', alpha=0.7, label='Lower limit (70%)')
    plt.title('Enhanced WGAN - Critic Accuracy', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Loss Difference
    plt.subplot(1, 3, 3)
    loss_diff = [g - c for g, c in zip(enhanced_wgan_trainer_new.history['gen_loss'], enhanced_wgan_trainer_new.history['critic_loss'])]
    plt.plot(epochs, loss_diff, label='Gen Loss - Critic Loss', color='purple', linewidth=2)
    plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)
    plt.title('Enhanced WGAN - Loss Balance', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss Difference')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()

Generating 6 samples per class for all 16 letter classes...

Epoch 1
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7167, Critic Loss: 33.2300, W-Loss: -0.3786, GP: 2.9746, Acc: 0.0469
Step  100/597 - Gen Loss: 2.1010, Critic Loss: -1.2574, W-Loss: -4.7796, GP: 0.1146, Acc: 0.4529
Step  200/597 - Gen Loss: -2.6419, Critic Loss: -2.7091, W-Loss: -5.1462, GP: 0.1050, Acc: 0.6857
Step  300/597 - Gen Loss: -6.9175, Critic Loss: -2.8593, W-Loss: -4.8888, GP: 0.1028, Acc: 0.7784
Step  400/597 - Gen Loss: -10.0971, Critic Loss: -2.9392, W-Loss: -4.7881, GP: 0.1052, Acc: 0.8283
Step  500/597 - Gen Loss: -12.8246, Critic Loss: -3.3547, W-Loss: -5.2347, GP: 0.1212, Acc: 0.8597

Epoch 2
------------------------------------------------------------
Step    0/597 - Gen Loss: -86.6805, Critic Loss: -11.9089, W-Loss: -14.6030, GP: 0.2604, Acc: 0.9969
Step  100/597 - Gen Loss: -96.9808, Critic Loss: -40.4296, W-Loss: -60.0598, GP: 1.9190, Acc: 0.9693
Step  200/597 - Gen Loss: -142.4918, Critic Loss: -81.5796, W-Loss: -127.5368, GP: 4.5006, Acc: 0.9502
Step  300/597 - Gen Loss: -282.2530, Critic Loss: -229.5090, W-Loss: -376.2630, GP: 14.3835, Acc: 0.9112
Step  400/597 - Gen Loss: -598.0519, Critic Loss: -512.4686, W-Loss: -883.1915, GP: 36.3976, Acc: 0.8682
Step  500/597 - Gen Loss: -783.2605, Critic Loss: -595.1448, W-Loss: -1113.9314, GP: 51.1531, Acc: 0.8618

Epoch 3
------------------------------------------------------------
Step    0/597 - Gen Loss: -4670.4731, Critic Loss: -3178.0317, W-Loss: -5961.8901, GP: 276.4044, Acc: 0.7531
Step  100/597 - Gen Loss: -5981.6719, Critic Loss: -5057.1304, W-Loss: -9474.3662, GP: 437.6936, Acc: 0.6610
Step  200/597 - Gen Loss: -7234.5049, Critic Loss: -5677.2612, W-Loss: -10360.4980, GP: 464.7543, Acc: 0.6914
Step  300/597 - Gen Loss: -9914.6504, Critic Loss: -5243.4536, W-Loss: -9532.2227, GP: 425.3211, Acc: 0.7174
Step  400/597 - Gen Loss: -10958.1836, Critic Loss: -3941.6558, W-Loss: -7162.2427, GP: 319.2760, Acc: 0.7712
Step  500/597 - Gen Loss: -11550.4727, Critic Loss: -3146.6531, W-Loss: -5725.3496, GP: 255.5672, Acc: 0.8047

Epoch 4
------------------------------------------------------------
Step    0/597 - Gen Loss: -13662.2021, Critic Loss: 259.2082, W-Loss: 256.6541, GP: 0.1231, Acc: 0.9531
Step  100/597 - Gen Loss: -8812.6445, Critic Loss: 29.4775, W-Loss: 26.6047, GP: 0.0919, Acc: 0.9556
Step  200/597 - Gen Loss: -9353.0586, Critic Loss: 5.2813, W-Loss: 2.5771, GP: 0.0823, Acc: 0.9577
Step  300/597 - Gen Loss: -9547.2920, Critic Loss: 11.1499, W-Loss: 8.4841, GP: 0.0796, Acc: 0.9577
Step  400/597 - Gen Loss: -8963.0068, Critic Loss: 0.4747, W-Loss: -2.2500, GP: 0.0773, Acc: 0.9555
Step  500/597 - Gen Loss: -8974.3477, Critic Loss: 1.0067, W-Loss: -1.7939, GP: 0.0765, Acc: 0.9544

Epoch 5
------------------------------------------------------------
Step    0/597 - Gen Loss: -12382.9053, Critic Loss: -254.4980, W-Loss: -257.5486, GP: 0.0223, Acc: 0.9500
Step  100/597 - Gen Loss: -14304.0820, Critic Loss: -0.1485, W-Loss: -3.4812, GP: 0.0763, Acc: 0.9532
Step  200/597 - Gen Loss: -14587.1738, Critic Loss: -39.1140, W-Loss: -42.4118, GP: 0.0817, Acc: 0.9559
Step  300/597 - Gen Loss: -14103.5947, Critic Loss: -3.3762, W-Loss: -6.6839, GP: 0.0802, Acc: 0.9544
Step  400/597 - Gen Loss: -13609.6484, Critic Loss: 3.3627, W-Loss: 0.0784, GP: 0.0823, Acc: 0.9540
Step  500/597 - Gen Loss: -13508.3877, Critic Loss: 8.0946, W-Loss: 4.7907, GP: 0.0820, Acc: 0.9532

Epoch 6
------------------------------------------------------------
Step    0/597 - Gen Loss: -13617.5479, Critic Loss: 87.0745, W-Loss: 82.4039, GP: 0.0323, Acc: 0.9438
Step  100/597 - Gen Loss: -16997.3477, Critic Loss: -58.4535, W-Loss: -62.0896, GP: 0.0860, Acc: 0.9533
Step  200/597 - Gen Loss: -17406.6250, Critic Loss: -9.3158, W-Loss: -13.0053, GP: 0.0936, Acc: 0.9544
Step  300/597 - Gen Loss: -16888.5176, Critic Loss: 1.8309, W-Loss: -1.8050, GP: 0.0887, Acc: 0.9541
Step  400/597 - Gen Loss: -16689.1738, Critic Loss: 2.4837, W-Loss: -1.0685, GP: 0.0869, Acc: 0.9545
Step  500/597 - Gen Loss: -16938.2188, Critic Loss: -5.5750, W-Loss: -9.1461, GP: 0.0915, Acc: 0.9556

Epoch 7
------------------------------------------------------------
Step    0/597 - Gen Loss: -16458.8711, Critic Loss: 482.6991, W-Loss: 478.8926, GP: 0.0297, Acc: 0.9656
Step  100/597 - Gen Loss: -15824.9912, Critic Loss: -6.3358, W-Loss: -9.2502, GP: 0.0845, Acc: 0.9674
Step  200/597 - Gen Loss: -17470.1973, Critic Loss: 12.3859, W-Loss: 9.2963, GP: 0.0859, Acc: 0.9666
Step  300/597 - Gen Loss: -17899.1875, Critic Loss: 3.1828, W-Loss: 0.0500, GP: 0.0828, Acc: 0.9667
Step  400/597 - Gen Loss: -16825.0332, Critic Loss: -5.1505, W-Loss: -8.3023, GP: 0.0836, Acc: 0.9661
Step  500/597 - Gen Loss: -16628.6484, Critic Loss: 6.7888, W-Loss: 3.6121, GP: 0.0837, Acc: 0.9660

Epoch 8
------------------------------------------------------------
Step    0/597 - Gen Loss: -20672.5840, Critic Loss: -1300.8015, W-Loss: -1304.1031, GP: 0.0286, Acc: 0.9594
Step  100/597 - Gen Loss: -20168.1055, Critic Loss: -24.7424, W-Loss: -27.8713, GP: 0.0830, Acc: 0.9712
Step  200/597 - Gen Loss: -21731.3379, Critic Loss: -73.7769, W-Loss: -77.3980, GP: 0.1000, Acc: 0.9692
Step  300/597 - Gen Loss: -23760.8301, Critic Loss: -115.0002, W-Loss: -119.0170, GP: 0.1061, Acc: 0.9673
Step  400/597 - Gen Loss: -25101.9941, Critic Loss: -40.7205, W-Loss: -44.8265, GP: 0.1046, Acc: 0.9667
Step  500/597 - Gen Loss: -25894.0332, Critic Loss: -52.7869, W-Loss: -56.8180, GP: 0.1049, Acc: 0.9675

Epoch 9
------------------------------------------------------------
Step    0/597 - Gen Loss: -30586.1133, Critic Loss: -629.5369, W-Loss: -635.8043, GP: 0.0338, Acc: 0.9563
Step  100/597 - Gen Loss: -28303.3555, Critic Loss: -44.0371, W-Loss: -49.0650, GP: 0.1489, Acc: 0.9666
Step  200/597 - Gen Loss: -28272.6719, Critic Loss: -89.3060, W-Loss: -94.2887, GP: 0.1418, Acc: 0.9668
Step  300/597 - Gen Loss: -28131.9238, Critic Loss: -88.8883, W-Loss: -93.8081, GP: 0.1397, Acc: 0.9681
Step  400/597 - Gen Loss: -27832.1094, Critic Loss: -48.8302, W-Loss: -53.5618, GP: 0.1360, Acc: 0.9685
Step  500/597 - Gen Loss: -27554.7285, Critic Loss: -39.1474, W-Loss: -43.8643, GP: 0.1364, Acc: 0.9685

Epoch 10
------------------------------------------------------------
Step    0/597 - Gen Loss: -25300.8398, Critic Loss: 420.3851, W-Loss: 417.4316, GP: 0.0363, Acc: 0.9750
Step  100/597 - Gen Loss: -23265.5645, Critic Loss: 117.0003, W-Loss: 112.6757, GP: 0.1052, Acc: 0.9690
Step  200/597 - Gen Loss: -20707.7715, Critic Loss: 72.5485, W-Loss: 68.3112, GP: 0.1033, Acc: 0.9700
Step  300/597 - Gen Loss: -18848.5059, Critic Loss: 84.9589, W-Loss: 80.7291, GP: 0.1021, Acc: 0.9702
Step  400/597 - Gen Loss: -17387.1523, Critic Loss: 76.6238, W-Loss: 72.5170, GP: 0.0988, Acc: 0.9705
Step  500/597 - Gen Loss: -17573.3105, Critic Loss: 28.0955, W-Loss: 24.0432, GP: 0.1000, Acc: 0.9711

Epoch 11
------------------------------------------------------------
Step    0/597 - Gen Loss: -23380.4512, Critic Loss: -2075.6108, W-Loss: -2078.4031, GP: 0.0385, Acc: 0.9844
Step  100/597 - Gen Loss: -24352.6016, Critic Loss: 28.4778, W-Loss: 23.9502, GP: 0.1234, Acc: 0.9741
Step  200/597 - Gen Loss: -25787.3281, Critic Loss: 2.6560, W-Loss: -1.7781, GP: 0.1273, Acc: 0.9759
Step  300/597 - Gen Loss: -25109.1387, Critic Loss: -13.9514, W-Loss: -18.1882, GP: 0.1251, Acc: 0.9764
Step  400/597 - Gen Loss: -23271.3066, Critic Loss: 28.8100, W-Loss: 24.8011, GP: 0.1193, Acc: 0.9769
Step  500/597 - Gen Loss: -23177.2695, Critic Loss: -15.3663, W-Loss: -19.5541, GP: 0.1186, Acc: 0.9762

Epoch 12
------------------------------------------------------------
Step    0/597 - Gen Loss: -23673.5957, Critic Loss: 226.2976, W-Loss: 224.3293, GP: 0.1323, Acc: 0.9875
Step  100/597 - Gen Loss: -26340.5684, Critic Loss: -81.8351, W-Loss: -85.5434, GP: 0.1240, Acc: 0.9808
Step  200/597 - Gen Loss: -28308.2344, Critic Loss: -63.0673, W-Loss: -66.9523, GP: 0.1311, Acc: 0.9808
Step  300/597 - Gen Loss: -29418.4824, Critic Loss: -101.4678, W-Loss: -105.4203, GP: 0.1290, Acc: 0.9806
Step  400/597 - Gen Loss: -30519.2891, Critic Loss: -16.7718, W-Loss: -20.9300, GP: 0.1293, Acc: 0.9797
Step  500/597 - Gen Loss: -31309.6250, Critic Loss: -33.6888, W-Loss: -38.1291, GP: 0.1275, Acc: 0.9780

Epoch 13
------------------------------------------------------------
Step    0/597 - Gen Loss: -38828.1250, Critic Loss: -796.6964, W-Loss: -801.9547, GP: 0.3665, Acc: 0.9875
Step  100/597 - Gen Loss: -38111.6094, Critic Loss: -6.7531, W-Loss: -12.0202, GP: 0.1528, Acc: 0.9759
Step  200/597 - Gen Loss: -41307.6094, Critic Loss: -27.7512, W-Loss: -33.0746, GP: 0.1583, Acc: 0.9763
Step  300/597 - Gen Loss: -42367.4688, Critic Loss: -75.4710, W-Loss: -80.7417, GP: 0.1597, Acc: 0.9770
Step  400/597 - Gen Loss: -42121.2031, Critic Loss: -5.5436, W-Loss: -10.7188, GP: 0.1485, Acc: 0.9767
Step  500/597 - Gen Loss: -40699.9844, Critic Loss: 23.0354, W-Loss: 17.8646, GP: 0.1438, Acc: 0.9765

Epoch 14
------------------------------------------------------------
Step    0/597 - Gen Loss: -38517.5508, Critic Loss: -1927.0022, W-Loss: -1934.7360, GP: 0.5617, Acc: 0.9906
Step  100/597 - Gen Loss: -37307.4727, Critic Loss: -101.6977, W-Loss: -106.7268, GP: 0.1623, Acc: 0.9798
Step  200/597 - Gen Loss: -37217.1797, Critic Loss: 45.1362, W-Loss: 40.4191, GP: 0.1464, Acc: 0.9806
Step  300/597 - Gen Loss: -37901.3594, Critic Loss: -68.5524, W-Loss: -73.2436, GP: 0.1464, Acc: 0.9804
Step  400/597 - Gen Loss: -37003.3555, Critic Loss: 6.3530, W-Loss: 1.6252, GP: 0.1419, Acc: 0.9801
Step  500/597 - Gen Loss: -36803.6406, Critic Loss: -27.5981, W-Loss: -32.3026, GP: 0.1409, Acc: 0.9800

Epoch 15
------------------------------------------------------------
Step    0/597 - Gen Loss: -41720.1953, Critic Loss: 192.4202, W-Loss: 187.9820, GP: 0.0441, Acc: 0.9781
Step  100/597 - Gen Loss: -47916.4648, Critic Loss: -465.1123, W-Loss: -470.7170, GP: 0.1599, Acc: 0.9795
Step  200/597 - Gen Loss: -49636.6797, Critic Loss: -7.5724, W-Loss: -13.1388, GP: 0.1614, Acc: 0.9802
Step  300/597 - Gen Loss: -49609.5625, Critic Loss: -194.9079, W-Loss: -200.2851, GP: 0.1633, Acc: 0.9808
Step  400/597 - Gen Loss: -50584.3594, Critic Loss: -87.5600, W-Loss: -92.8462, GP: 0.1631, Acc: 0.9812
Step  500/597 - Gen Loss: -50534.5547, Critic Loss: -42.0485, W-Loss: -47.2682, GP: 0.1640, Acc: 0.9814

Epoch 16
------------------------------------------------------------
Step    0/597 - Gen Loss: -50350.1875, Critic Loss: -2571.9956, W-Loss: -2577.5750, GP: 0.0462, Acc: 0.9812
Step  100/597 - Gen Loss: -56696.1250, Critic Loss: -346.8472, W-Loss: -352.8972, GP: 0.2123, Acc: 0.9814
Step  200/597 - Gen Loss: -52856.5234, Critic Loss: -94.5564, W-Loss: -100.6388, GP: 0.2042, Acc: 0.9820
Step  300/597 - Gen Loss: -52640.7305, Critic Loss: -247.2421, W-Loss: -253.2995, GP: 0.2039, Acc: 0.9824
Step  400/597 - Gen Loss: -54293.1406, Critic Loss: -155.7221, W-Loss: -161.9988, GP: 0.2075, Acc: 0.9812
Step  500/597 - Gen Loss: -55089.5234, Critic Loss: -89.4649, W-Loss: -95.8331, GP: 0.2036, Acc: 0.9806

Epoch 17
------------------------------------------------------------
Step    0/597 - Gen Loss: -57243.0742, Critic Loss: 3017.8516, W-Loss: 3015.0891, GP: 0.0440, Acc: 0.9844
Step  100/597 - Gen Loss: -57377.1133, Critic Loss: 564.8786, W-Loss: 557.8820, GP: 0.2286, Acc: 0.9797
Step  200/597 - Gen Loss: -57328.1094, Critic Loss: 60.6666, W-Loss: 53.6368, GP: 0.2234, Acc: 0.9794
Step  300/597 - Gen Loss: -56756.7656, Critic Loss: -69.6664, W-Loss: -76.8506, GP: 0.2206, Acc: 0.9788
Step  400/597 - Gen Loss: -57798.3828, Critic Loss: -87.6272, W-Loss: -94.9635, GP: 0.2230, Acc: 0.9787
Step  500/597 - Gen Loss: -58888.8477, Critic Loss: -22.2883, W-Loss: -29.5428, GP: 0.2211, Acc: 0.9789

Epoch 18
------------------------------------------------------------
Step    0/597 - Gen Loss: -63384.4727, Critic Loss: -3100.4421, W-Loss: -3102.4507, GP: 0.0357, Acc: 0.9781
Step  100/597 - Gen Loss: -59800.5156, Critic Loss: 157.6357, W-Loss: 151.0717, GP: 0.2230, Acc: 0.9817
Step  200/597 - Gen Loss: -61884.6719, Critic Loss: 40.1487, W-Loss: 33.0402, GP: 0.2429, Acc: 0.9807
Step  300/597 - Gen Loss: -63034.1133, Critic Loss: -349.7399, W-Loss: -357.2279, GP: 0.2549, Acc: 0.9806
Step  400/597 - Gen Loss: -64586.0000, Critic Loss: -134.8826, W-Loss: -142.7921, GP: 0.2564, Acc: 0.9796
Step  500/597 - Gen Loss: -66164.6250, Critic Loss: -172.4624, W-Loss: -180.5146, GP: 0.2556, Acc: 0.9791

Epoch 19
------------------------------------------------------------
Step    0/597 - Gen Loss: -74807.9531, Critic Loss: 3814.1128, W-Loss: 3808.1797, GP: 0.0591, Acc: 0.9906
Step  100/597 - Gen Loss: -82609.3281, Critic Loss: 126.0955, W-Loss: 117.5596, GP: 0.3024, Acc: 0.9821
Step  200/597 - Gen Loss: -81496.3359, Critic Loss: 68.5711, W-Loss: 60.1915, GP: 0.2841, Acc: 0.9811
Step  300/597 - Gen Loss: -78903.3594, Critic Loss: -311.6729, W-Loss: -319.7769, GP: 0.2847, Acc: 0.9817
Step  400/597 - Gen Loss: -78170.3828, Critic Loss: -27.8845, W-Loss: -35.7989, GP: 0.2834, Acc: 0.9819
Step  500/597 - Gen Loss: -76587.5156, Critic Loss: -82.3007, W-Loss: -90.1759, GP: 0.2853, Acc: 0.9819

Epoch 20
------------------------------------------------------------
Step    0/597 - Gen Loss: -75609.6094, Critic Loss: -15023.5107, W-Loss: -15030.4971, GP: 0.1101, Acc: 0.9875
Step  100/597 - Gen Loss: -67420.8438, Critic Loss: 131.5867, W-Loss: 121.9563, GP: 0.3089, Acc: 0.9776
Step  200/597 - Gen Loss: -66191.2109, Critic Loss: 198.2640, W-Loss: 189.0795, GP: 0.3284, Acc: 0.9794
Step  300/597 - Gen Loss: -60826.3438, Critic Loss: -9.1371, W-Loss: -17.8807, GP: 0.3195, Acc: 0.9800
Step  400/597 - Gen Loss: -57690.7891, Critic Loss: 243.9167, W-Loss: 235.5121, GP: 0.3077, Acc: 0.9803
Step  500/597 - Gen Loss: -55511.6602, Critic Loss: 55.5079, W-Loss: 47.2322, GP: 0.3027, Acc: 0.9805

Epoch 21
------------------------------------------------------------
Step    0/597 - Gen Loss: -76004.3125, Critic Loss: -10649.7129, W-Loss: -10658.1660, GP: 0.3175, Acc: 0.9844
Step  100/597 - Gen Loss: -68918.1094, Critic Loss: 147.8934, W-Loss: 140.3162, GP: 0.3162, Acc: 0.9844
Step  200/597 - Gen Loss: -69190.8281, Critic Loss: -90.1557, W-Loss: -98.1506, GP: 0.3382, Acc: 0.9841
Step  300/597 - Gen Loss: -69091.1328, Critic Loss: -271.9482, W-Loss: -279.9930, GP: 0.3211, Acc: 0.9832
Step  400/597 - Gen Loss: -71764.8750, Critic Loss: -182.3465, W-Loss: -190.4660, GP: 0.3180, Acc: 0.9831
Step  500/597 - Gen Loss: -73353.6953, Critic Loss: -315.2722, W-Loss: -323.5867, GP: 0.3150, Acc: 0.9826

Epoch 22
------------------------------------------------------------
Step    0/597 - Gen Loss: -102564.7422, Critic Loss: 16175.6621, W-Loss: 16159.6172, GP: 0.7151, Acc: 0.9781
Step  100/597 - Gen Loss: -90823.5625, Critic Loss: 386.5254, W-Loss: 375.8613, GP: 0.3647, Acc: 0.9804
Step  200/597 - Gen Loss: -93782.5000, Critic Loss: -206.1927, W-Loss: -217.4782, GP: 0.3970, Acc: 0.9797
Step  300/597 - Gen Loss: -91082.0938, Critic Loss: 1.8338, W-Loss: -9.1041, GP: 0.4103, Acc: 0.9803
Step  400/597 - Gen Loss: -87120.9844, Critic Loss: 135.1419, W-Loss: 124.3412, GP: 0.4022, Acc: 0.9803
Step  500/597 - Gen Loss: -85506.4844, Critic Loss: 29.3554, W-Loss: 18.5799, GP: 0.3879, Acc: 0.9795

Epoch 23
------------------------------------------------------------
Step    0/597 - Gen Loss: -92664.0703, Critic Loss: -3243.7290, W-Loss: -3247.6765, GP: 0.0531, Acc: 0.9844
Step  100/597 - Gen Loss: -102062.5156, Critic Loss: 5.5750, W-Loss: -4.1771, GP: 0.2778, Acc: 0.9811
Step  200/597 - Gen Loss: -93933.4609, Critic Loss: 551.4724, W-Loss: 541.9943, GP: 0.2971, Acc: 0.9813
Step  300/597 - Gen Loss: -91223.3594, Critic Loss: -329.9706, W-Loss: -339.6768, GP: 0.3093, Acc: 0.9809
Step  400/597 - Gen Loss: -90839.4688, Critic Loss: 73.0164, W-Loss: 63.4503, GP: 0.2978, Acc: 0.9809
Step  500/597 - Gen Loss: -91440.9609, Critic Loss: -67.2012, W-Loss: -76.8337, GP: 0.3064, Acc: 0.9809

Epoch 24
------------------------------------------------------------
Step    0/597 - Gen Loss: -109458.6484, Critic Loss: 11880.2051, W-Loss: 11873.5566, GP: 0.1313, Acc: 0.9844
Step  100/597 - Gen Loss: -123289.2891, Critic Loss: -281.2260, W-Loss: -293.4010, GP: 0.3286, Acc: 0.9768
Step  200/597 - Gen Loss: -127626.9062, Critic Loss: -291.0522, W-Loss: -303.5034, GP: 0.3445, Acc: 0.9781
Step  300/597 - Gen Loss: -126426.0703, Critic Loss: -1106.4806, W-Loss: -1118.7548, GP: 0.3594, Acc: 0.9785
Step  400/597 - Gen Loss: -130278.0469, Critic Loss: -475.0466, W-Loss: -487.5669, GP: 0.3750, Acc: 0.9783
Step  500/597 - Gen Loss: -133265.4844, Critic Loss: -710.1071, W-Loss: -722.9640, GP: 0.3917, Acc: 0.9782

Epoch 25
------------------------------------------------------------
Step    0/597 - Gen Loss: -144947.4688, Critic Loss: 9895.6943, W-Loss: 9879.7998, GP: 0.0795, Acc: 0.9781
Step  100/597 - Gen Loss: -157964.4375, Critic Loss: 12.8840, W-Loss: -5.3693, GP: 0.5236, Acc: 0.9735
Step  200/597 - Gen Loss: -171242.9375, Critic Loss: 643.7300, W-Loss: 624.9692, GP: 0.4979, Acc: 0.9731
Step  300/597 - Gen Loss: -168619.3281, Critic Loss: -1147.8461, W-Loss: -1166.6741, GP: 0.4992, Acc: 0.9728
Step  400/597 - Gen Loss: -169376.2344, Critic Loss: -1008.9179, W-Loss: -1028.1578, GP: 0.5030, Acc: 0.9720
Step  500/597 - Gen Loss: -176080.2969, Critic Loss: -1035.0625, W-Loss: -1054.5791, GP: 0.5033, Acc: 0.9714

Epoch 26
------------------------------------------------------------
Step    0/597 - Gen Loss: -183721.8125, Critic Loss: 1951.7078, W-Loss: 1904.0750, GP: 2.4617, Acc: 0.9594
Step  100/597 - Gen Loss: -180795.5625, Critic Loss: 701.2108, W-Loss: 676.5483, GP: 0.6097, Acc: 0.9680
Step  200/597 - Gen Loss: -166383.4062, Critic Loss: 1327.6206, W-Loss: 1304.1063, GP: 0.5726, Acc: 0.9681
Step  300/597 - Gen Loss: -162378.5469, Critic Loss: 81.2755, W-Loss: 59.0144, GP: 0.5470, Acc: 0.9692
Step  400/597 - Gen Loss: -163864.6406, Critic Loss: 586.7202, W-Loss: 565.3774, GP: 0.5363, Acc: 0.9705
Step  500/597 - Gen Loss: -167832.5625, Critic Loss: -35.8559, W-Loss: -56.4861, GP: 0.5214, Acc: 0.9711

Epoch 27
------------------------------------------------------------
Step    0/597 - Gen Loss: -183116.0156, Critic Loss: -22219.5000, W-Loss: -22241.9062, GP: 0.1554, Acc: 0.9656
Step  100/597 - Gen Loss: -256930.3438, Critic Loss: 2264.1482, W-Loss: 2234.6252, GP: 0.8470, Acc: 0.9672
Step  200/597 - Gen Loss: -247217.0781, Critic Loss: 1975.4230, W-Loss: 1949.3900, GP: 0.7986, Acc: 0.9703
Step  300/597 - Gen Loss: -234745.7812, Critic Loss: 576.3001, W-Loss: 552.3591, GP: 0.7447, Acc: 0.9713
Step  400/597 - Gen Loss: -235862.1250, Critic Loss: 461.7574, W-Loss: 438.2850, GP: 0.7528, Acc: 0.9717
Step  500/597 - Gen Loss: -239211.0156, Critic Loss: 144.4145, W-Loss: 121.2388, GP: 0.7386, Acc: 0.9719

Epoch 28
------------------------------------------------------------
Step    0/597 - Gen Loss: -251425.9531, Critic Loss: -53841.3516, W-Loss: -53861.4141, GP: 0.4671, Acc: 0.9625
Step  100/597 - Gen Loss: -272933.0312, Critic Loss: -602.4647, W-Loss: -633.7369, GP: 0.8976, Acc: 0.9670
Step  200/597 - Gen Loss: -268260.3125, Critic Loss: 627.8726, W-Loss: 597.3129, GP: 0.8862, Acc: 0.9674
Step  300/597 - Gen Loss: -262896.5625, Critic Loss: -1689.8218, W-Loss: -1719.1450, GP: 0.8842, Acc: 0.9686
Step  400/597 - Gen Loss: -258442.5156, Critic Loss: -868.4567, W-Loss: -897.6013, GP: 0.9233, Acc: 0.9690
Step  500/597 - Gen Loss: -255824.0156, Critic Loss: -1612.6184, W-Loss: -1641.9011, GP: 0.9482, Acc: 0.9693

Epoch 29
------------------------------------------------------------
Step    0/597 - Gen Loss: -294096.1875, Critic Loss: -18102.8477, W-Loss: -18125.6504, GP: 0.0923, Acc: 0.9688
Step  100/597 - Gen Loss: -330186.5000, Critic Loss: -3905.9971, W-Loss: -3951.2349, GP: 1.5572, Acc: 0.9626
Step  200/597 - Gen Loss: -317174.3438, Critic Loss: -3503.5686, W-Loss: -3548.1201, GP: 1.6199, Acc: 0.9632
Step  300/597 - Gen Loss: -313829.9688, Critic Loss: -4159.4902, W-Loss: -4204.1040, GP: 1.6017, Acc: 0.9630
Step  400/597 - Gen Loss: -325780.8750, Critic Loss: -2667.8879, W-Loss: -2712.8035, GP: 1.5843, Acc: 0.9625
Step  500/597 - Gen Loss: -337403.8438, Critic Loss: -3076.8047, W-Loss: -3122.1243, GP: 1.5705, Acc: 0.9619

Epoch 30
------------------------------------------------------------
Step    0/597 - Gen Loss: -428664.4688, Critic Loss: -31740.1367, W-Loss: -31786.1680, GP: 0.1127, Acc: 0.9500
Step  100/597 - Gen Loss: -449782.5625, Critic Loss: -800.6113, W-Loss: -861.1158, GP: 1.9668, Acc: 0.9523
Step  200/597 - Gen Loss: -473022.8750, Critic Loss: 1075.6787, W-Loss: 1016.3713, GP: 1.8941, Acc: 0.9520
Step  300/597 - Gen Loss: -471442.5000, Critic Loss: -1247.7964, W-Loss: -1304.4181, GP: 1.7973, Acc: 0.9539
Step  400/597 - Gen Loss: -466272.6250, Critic Loss: 976.9465, W-Loss: 922.9463, GP: 1.7475, Acc: 0.9556
Step  500/597 - Gen Loss: -459806.8438, Critic Loss: -362.6032, W-Loss: -414.1882, GP: 1.6645, Acc: 0.9572

Epoch 31
------------------------------------------------------------
Step    0/597 - Gen Loss: -475847.8750, Critic Loss: -45586.6797, W-Loss: -45627.9766, GP: 0.1219, Acc: 0.9656
Step  100/597 - Gen Loss: -494646.7188, Critic Loss: 3815.5813, W-Loss: 3755.9443, GP: 1.7410, Acc: 0.9563
Step  200/597 - Gen Loss: -478431.4375, Critic Loss: 2061.6167, W-Loss: 2005.7374, GP: 1.7543, Acc: 0.9580
Step  300/597 - Gen Loss: -477491.0312, Critic Loss: -2509.7810, W-Loss: -2563.5820, GP: 1.7261, Acc: 0.9584
Step  400/597 - Gen Loss: -489856.6875, Critic Loss: -596.2393, W-Loss: -650.3376, GP: 1.7105, Acc: 0.9582
Step  500/597 - Gen Loss: -506996.7188, Critic Loss: -1237.0322, W-Loss: -1291.4202, GP: 1.6795, Acc: 0.9577

Epoch 32
------------------------------------------------------------
Step    0/597 - Gen Loss: -622965.1875, Critic Loss: 18184.7363, W-Loss: 18109.7617, GP: 3.6973, Acc: 0.9656
Step  100/597 - Gen Loss: -561139.8125, Critic Loss: -17.4066, W-Loss: -73.8957, GP: 1.8082, Acc: 0.9574
Step  200/597 - Gen Loss: -558194.1875, Critic Loss: 2090.9856, W-Loss: 2035.7196, GP: 1.7581, Acc: 0.9574
Step  300/597 - Gen Loss: -540324.1250, Critic Loss: -1950.4199, W-Loss: -2005.3887, GP: 1.8076, Acc: 0.9580
Step  400/597 - Gen Loss: -545837.0625, Critic Loss: 463.4841, W-Loss: 408.5214, GP: 1.7960, Acc: 0.9583
Step  500/597 - Gen Loss: -550958.0625, Critic Loss: -624.1193, W-Loss: -677.8334, GP: 1.7682, Acc: 0.9592

Epoch 33
------------------------------------------------------------
Step    0/597 - Gen Loss: -635976.9375, Critic Loss: 50721.0039, W-Loss: 50663.2109, GP: 0.4600, Acc: 0.9406
Step  100/597 - Gen Loss: -584246.3750, Critic Loss: -4643.0977, W-Loss: -4718.1997, GP: 2.4196, Acc: 0.9523
Step  200/597 - Gen Loss: -554619.1250, Critic Loss: 1099.8219, W-Loss: 1028.8320, GP: 2.4397, Acc: 0.9549
Step  300/597 - Gen Loss: -538190.6875, Critic Loss: -2895.8811, W-Loss: -2965.1895, GP: 2.4788, Acc: 0.9569
Step  400/597 - Gen Loss: -562300.4375, Critic Loss: -1652.8669, W-Loss: -1724.0557, GP: 2.5195, Acc: 0.9563
Step  500/597 - Gen Loss: -580082.8125, Critic Loss: -3084.8843, W-Loss: -3155.9131, GP: 2.5115, Acc: 0.9563

Epoch 34
------------------------------------------------------------
Step    0/597 - Gen Loss: -560130.3750, Critic Loss: -76268.0469, W-Loss: -76338.0781, GP: 1.0219, Acc: 0.9469
Step  100/597 - Gen Loss: -756952.8750, Critic Loss: 1824.7418, W-Loss: 1720.5886, GP: 3.2420, Acc: 0.9408
Step  200/597 - Gen Loss: -767875.7500, Critic Loss: 1789.6130, W-Loss: 1693.2338, GP: 3.0387, Acc: 0.9441
Step  300/597 - Gen Loss: -752344.7500, Critic Loss: -2449.9126, W-Loss: -2542.4805, GP: 3.0356, Acc: 0.9465
Step  400/597 - Gen Loss: -765746.1250, Critic Loss: -2101.0754, W-Loss: -2193.3689, GP: 3.0170, Acc: 0.9465
Step  500/597 - Gen Loss: -777772.1875, Critic Loss: -855.2665, W-Loss: -947.0332, GP: 3.0191, Acc: 0.9466

Epoch 35
------------------------------------------------------------
Step    0/597 - Gen Loss: -773469.6875, Critic Loss: 10933.4785, W-Loss: 10708.1621, GP: 16.2524, Acc: 0.9531
Step  100/597 - Gen Loss: -767088.6250, Critic Loss: 4319.9985, W-Loss: 4217.9517, GP: 3.5273, Acc: 0.9469
Step  200/597 - Gen Loss: -787969.5000, Critic Loss: 7633.7275, W-Loss: 7536.8560, GP: 3.3202, Acc: 0.9476
Step  300/597 - Gen Loss: -778988.3125, Critic Loss: -853.4112, W-Loss: -946.2977, GP: 3.2504, Acc: 0.9490
Step  400/597 - Gen Loss: -802210.6875, Critic Loss: 335.1146, W-Loss: 241.6962, GP: 3.1477, Acc: 0.9480
Step  500/597 - Gen Loss: -812596.7500, Critic Loss: -662.6339, W-Loss: -754.9269, GP: 3.0696, Acc: 0.9489

Epoch 36
------------------------------------------------------------
Step    0/597 - Gen Loss: -793764.2500, Critic Loss: 50824.8711, W-Loss: 50617.6875, GP: 11.9495, Acc: 0.9187
Step  100/597 - Gen Loss: -870761.5000, Critic Loss: -8679.8174, W-Loss: -8801.0322, GP: 3.8415, Acc: 0.9390
Step  200/597 - Gen Loss: -866224.3125, Critic Loss: 4448.7109, W-Loss: 4340.9746, GP: 3.4119, Acc: 0.9427
Step  300/597 - Gen Loss: -845012.1875, Critic Loss: 497.8233, W-Loss: 396.7625, GP: 3.4135, Acc: 0.9458
Step  400/597 - Gen Loss: -846175.8125, Critic Loss: 80.4248, W-Loss: -20.8514, GP: 3.4223, Acc: 0.9462
Step  500/597 - Gen Loss: -850639.1875, Critic Loss: -2978.5813, W-Loss: -3080.7312, GP: 3.4415, Acc: 0.9461

Epoch 37
------------------------------------------------------------
Step    0/597 - Gen Loss: -784277.8750, Critic Loss: -158035.8750, W-Loss: -158213.7500, GP: 11.6397, Acc: 0.9656
Step  100/597 - Gen Loss: -1005912.3125, Critic Loss: -7435.5464, W-Loss: -7575.7944, GP: 4.2841, Acc: 0.9378
Step  200/597 - Gen Loss: -1055176.2500, Critic Loss: -3880.8660, W-Loss: -4019.9836, GP: 4.2886, Acc: 0.9373
Step  300/597 - Gen Loss: -1036261.1250, Critic Loss: -6892.7012, W-Loss: -7023.3813, GP: 4.0284, Acc: 0.9388
Step  400/597 - Gen Loss: -1031650.8750, Critic Loss: -3419.1316, W-Loss: -3546.6804, GP: 3.9875, Acc: 0.9402
Step  500/597 - Gen Loss: -1026001.3750, Critic Loss: -6373.2573, W-Loss: -6500.6934, GP: 4.0342, Acc: 0.9402

Epoch 38
------------------------------------------------------------
Step    0/597 - Gen Loss: -756945.1250, Critic Loss: -25983.6152, W-Loss: -26051.7500, GP: 0.2133, Acc: 0.9531
Step  100/597 - Gen Loss: -1122305.2500, Critic Loss: -12005.1562, W-Loss: -12157.9238, GP: 4.4370, Acc: 0.9319
Step  200/597 - Gen Loss: -1143930.6250, Critic Loss: -1891.4482, W-Loss: -2046.4236, GP: 4.7909, Acc: 0.9324
Step  300/597 - Gen Loss: -1098878.8750, Critic Loss: -7292.5376, W-Loss: -7442.9707, GP: 4.8969, Acc: 0.9352
Step  400/597 - Gen Loss: -1081516.8750, Critic Loss: -5152.9414, W-Loss: -5299.5029, GP: 4.7373, Acc: 0.9358
Step  500/597 - Gen Loss: -1071030.0000, Critic Loss: -6369.1675, W-Loss: -6514.7212, GP: 4.7642, Acc: 0.9361

Epoch 39
------------------------------------------------------------
Step    0/597 - Gen Loss: -1023746.9375, Critic Loss: 18848.8633, W-Loss: 18759.5996, GP: 0.1926, Acc: 0.9438
Step  100/597 - Gen Loss: -1236544.3750, Critic Loss: -4313.1479, W-Loss: -4499.8213, GP: 5.7056, Acc: 0.9273
Step  200/597 - Gen Loss: -1269786.1250, Critic Loss: 6900.1528, W-Loss: 6715.3306, GP: 5.7684, Acc: 0.9269
Step  300/597 - Gen Loss: -1257984.0000, Critic Loss: 283.9683, W-Loss: 113.2171, GP: 5.3049, Acc: 0.9298
Step  400/597 - Gen Loss: -1244893.6250, Critic Loss: 2756.3755, W-Loss: 2590.2239, GP: 5.2215, Acc: 0.9308
Step  500/597 - Gen Loss: -1244310.0000, Critic Loss: 1068.3558, W-Loss: 906.8563, GP: 5.1398, Acc: 0.9320

Epoch 40
------------------------------------------------------------
Step    0/597 - Gen Loss: -1424203.5000, Critic Loss: 54021.2383, W-Loss: 53903.4258, GP: 0.2505, Acc: 0.9438
Step  100/597 - Gen Loss: -1316783.6250, Critic Loss: -16123.7451, W-Loss: -16317.8652, GP: 5.5293, Acc: 0.9219
Step  200/597 - Gen Loss: -1357374.1250, Critic Loss: -966.2819, W-Loss: -1154.9509, GP: 5.3637, Acc: 0.9229
Step  300/597 - Gen Loss: -1334001.6250, Critic Loss: -4009.3813, W-Loss: -4187.5981, GP: 5.3274, Acc: 0.9262
Step  400/597 - Gen Loss: -1340949.2500, Critic Loss: 3486.8301, W-Loss: 3310.6672, GP: 5.4323, Acc: 0.9272
Step  500/597 - Gen Loss: -1346868.5000, Critic Loss: 1889.8473, W-Loss: 1719.1547, GP: 5.2953, Acc: 0.9290

Epoch 41
------------------------------------------------------------
Step    0/597 - Gen Loss: -1491935.2500, Critic Loss: -140766.3906, W-Loss: -140916.1562, GP: 4.4768, Acc: 0.9312
Step  100/597 - Gen Loss: -1369227.6250, Critic Loss: -58.3916, W-Loss: -231.9572, GP: 5.3668, Acc: 0.9284
Step  200/597 - Gen Loss: -1392899.6250, Critic Loss: 11850.8555, W-Loss: 11682.5283, GP: 5.2678, Acc: 0.9306
Step  300/597 - Gen Loss: -1350118.7500, Critic Loss: 5693.1885, W-Loss: 5536.8086, GP: 5.0746, Acc: 0.9346
Step  400/597 - Gen Loss: -1336028.2500, Critic Loss: 9685.6074, W-Loss: 9535.9600, GP: 4.9243, Acc: 0.9366
Step  500/597 - Gen Loss: -1350258.8750, Critic Loss: 2157.9871, W-Loss: 2009.7860, GP: 4.8593, Acc: 0.9373

Epoch 42
------------------------------------------------------------
Step    0/597 - Gen Loss: -1613547.6250, Critic Loss: -401549.4375, W-Loss: -401643.4688, GP: 0.4807, Acc: 0.9312
Step  100/597 - Gen Loss: -1551730.5000, Critic Loss: -18699.4531, W-Loss: -18862.4434, GP: 5.1693, Acc: 0.9340
Step  200/597 - Gen Loss: -1521887.0000, Critic Loss: -10848.1758, W-Loss: -11019.0215, GP: 5.5882, Acc: 0.9332
Step  300/597 - Gen Loss: -1468184.3750, Critic Loss: -14513.4404, W-Loss: -14682.2588, GP: 5.5307, Acc: 0.9342
Step  400/597 - Gen Loss: -1507169.6250, Critic Loss: -10393.9980, W-Loss: -10565.1328, GP: 5.5522, Acc: 0.9332
Step  500/597 - Gen Loss: -1555776.0000, Critic Loss: -12895.3193, W-Loss: -13068.1494, GP: 5.6253, Acc: 0.9334

Epoch 43
------------------------------------------------------------
Step    0/597 - Gen Loss: -1522913.1250, Critic Loss: 120244.9141, W-Loss: 120081.5781, GP: 0.7189, Acc: 0.9438
Step  100/597 - Gen Loss: -1576830.6250, Critic Loss: 6977.1455, W-Loss: 6788.7520, GP: 6.4416, Acc: 0.9344
Step  200/597 - Gen Loss: -1551062.1250, Critic Loss: 1628.5188, W-Loss: 1437.9869, GP: 6.3961, Acc: 0.9332
Step  300/597 - Gen Loss: -1499043.2500, Critic Loss: -5612.9912, W-Loss: -5801.1699, GP: 6.3567, Acc: 0.9340
Step  400/597 - Gen Loss: -1510692.8750, Critic Loss: -3656.7922, W-Loss: -3847.2258, GP: 6.5280, Acc: 0.9339
Step  500/597 - Gen Loss: -1508548.8750, Critic Loss: -3535.3005, W-Loss: -3724.0024, GP: 6.3877, Acc: 0.9342

Epoch 44
------------------------------------------------------------
Step    0/597 - Gen Loss: -1733452.1250, Critic Loss: 195599.5469, W-Loss: 195358.2031, GP: 6.2223, Acc: 0.9187
Step  100/597 - Gen Loss: -1717611.7500, Critic Loss: -2299.0327, W-Loss: -2525.0923, GP: 6.6679, Acc: 0.9200
Step  200/597 - Gen Loss: -1736528.3750, Critic Loss: -1677.5138, W-Loss: -1914.8732, GP: 6.8867, Acc: 0.9194
Step  300/597 - Gen Loss: -1705106.5000, Critic Loss: -9636.4160, W-Loss: -9873.3213, GP: 6.8990, Acc: 0.9197
Step  400/597 - Gen Loss: -1728699.7500, Critic Loss: -2921.2048, W-Loss: -3155.5579, GP: 6.7917, Acc: 0.9199
Step  500/597 - Gen Loss: -1756903.2500, Critic Loss: -13200.2256, W-Loss: -13436.1934, GP: 6.8901, Acc: 0.9199

Epoch 45
------------------------------------------------------------
Step    0/597 - Gen Loss: -1581362.3750, Critic Loss: 92452.8906, W-Loss: 91851.9531, GP: 45.3985, Acc: 0.9312
Step  100/597 - Gen Loss: -1912846.2500, Critic Loss: 10217.4336, W-Loss: 9951.7832, GP: 8.6135, Acc: 0.9147
Step  200/597 - Gen Loss: -1983845.3750, Critic Loss: 1820.5465, W-Loss: 1565.4093, GP: 8.2100, Acc: 0.9175
Step  300/597 - Gen Loss: -1980057.1250, Critic Loss: -15305.5225, W-Loss: -15554.3896, GP: 8.0499, Acc: 0.9188
Step  400/597 - Gen Loss: -2005652.0000, Critic Loss: -16618.5039, W-Loss: -16869.8555, GP: 8.5230, Acc: 0.9196
Step  500/597 - Gen Loss: -2035727.6250, Critic Loss: -11254.6582, W-Loss: -11505.6650, GP: 8.5909, Acc: 0.9204

Epoch 46
------------------------------------------------------------
Step    0/597 - Gen Loss: -1742237.5000, Critic Loss: 1940.2719, W-Loss: 1753.3500, GP: 1.3018, Acc: 0.9500
Step  100/597 - Gen Loss: -2253974.5000, Critic Loss: -30344.5742, W-Loss: -30651.2070, GP: 9.4199, Acc: 0.9089
Step  200/597 - Gen Loss: -2239575.7500, Critic Loss: 10394.9766, W-Loss: 10090.7090, GP: 9.6325, Acc: 0.9093
Step  300/597 - Gen Loss: -2225093.0000, Critic Loss: -726.5741, W-Loss: -1020.5170, GP: 9.3494, Acc: 0.9100
Step  400/597 - Gen Loss: -2232213.5000, Critic Loss: -413.4816, W-Loss: -703.2023, GP: 9.1406, Acc: 0.9105
Step  500/597 - Gen Loss: -2240073.0000, Critic Loss: -4090.3645, W-Loss: -4375.1240, GP: 8.9964, Acc: 0.9114

Epoch 47
------------------------------------------------------------
Step    0/597 - Gen Loss: -1563098.3750, Critic Loss: -637946.0625, W-Loss: -638142.9375, GP: 0.3544, Acc: 0.9000
Step  100/597 - Gen Loss: -2345726.2500, Critic Loss: 12597.7559, W-Loss: 12265.2236, GP: 11.6136, Acc: 0.9078
Step  200/597 - Gen Loss: -2373745.7500, Critic Loss: 10769.4795, W-Loss: 10445.4824, GP: 11.5985, Acc: 0.9091
Step  300/597 - Gen Loss: -2408913.5000, Critic Loss: -14180.1211, W-Loss: -14500.0693, GP: 11.2962, Acc: 0.9101
Step  400/597 - Gen Loss: -2451198.5000, Critic Loss: -8055.6309, W-Loss: -8374.2939, GP: 11.1088, Acc: 0.9095
Step  500/597 - Gen Loss: -2420810.0000, Critic Loss: -2852.0498, W-Loss: -3167.7544, GP: 11.0480, Acc: 0.9108

Epoch 48
------------------------------------------------------------
Step    0/597 - Gen Loss: -1763271.3750, Critic Loss: 292832.5312, W-Loss: 292663.3750, GP: 1.7185, Acc: 0.9406
Step  100/597 - Gen Loss: -2366226.5000, Critic Loss: 18342.8203, W-Loss: 18007.4180, GP: 12.4494, Acc: 0.9110
Step  200/597 - Gen Loss: -2317468.0000, Critic Loss: 27450.3574, W-Loss: 27132.7363, GP: 11.2680, Acc: 0.9122
Step  300/597 - Gen Loss: -2188874.0000, Critic Loss: -4005.7847, W-Loss: -4313.1396, GP: 10.9518, Acc: 0.9134
Step  400/597 - Gen Loss: -2176339.5000, Critic Loss: 3786.9407, W-Loss: 3481.6948, GP: 10.7995, Acc: 0.9142
Step  500/597 - Gen Loss: -2169994.0000, Critic Loss: 2514.9136, W-Loss: 2213.3904, GP: 10.7127, Acc: 0.9147

Epoch 49
------------------------------------------------------------
Step    0/597 - Gen Loss: -2874546.7500, Critic Loss: 255544.8281, W-Loss: 255317.4531, GP: 0.1973, Acc: 0.9062
Step  100/597 - Gen Loss: -2478010.7500, Critic Loss: -5508.9673, W-Loss: -5875.7310, GP: 12.5463, Acc: 0.9026
Step  200/597 - Gen Loss: -2461892.5000, Critic Loss: 10507.3975, W-Loss: 10155.0928, GP: 12.1797, Acc: 0.9056
Step  300/597 - Gen Loss: -2375004.0000, Critic Loss: -313.9174, W-Loss: -654.7145, GP: 11.6865, Acc: 0.9064
Step  400/597 - Gen Loss: -2353695.0000, Critic Loss: 6889.4692, W-Loss: 6557.1958, GP: 11.3695, Acc: 0.9072
Step  500/597 - Gen Loss: -2327234.7500, Critic Loss: -352.0511, W-Loss: -678.1369, GP: 11.1023, Acc: 0.9080

Epoch 50
------------------------------------------------------------
Step    0/597 - Gen Loss: -593770.4375, Critic Loss: 137621.7812, W-Loss: 137412.7031, GP: 0.1847, Acc: 0.9094
Step  100/597 - Gen Loss: -2324717.5000, Critic Loss: -42510.2422, W-Loss: -42860.3906, GP: 12.4381, Acc: 0.9065
Step  200/597 - Gen Loss: -2339635.2500, Critic Loss: -35561.8438, W-Loss: -35917.3281, GP: 12.3743, Acc: 0.9051
Step  300/597 - Gen Loss: -2355899.7500, Critic Loss: -41214.9219, W-Loss: -41567.6758, GP: 12.1067, Acc: 0.9046
Step  400/597 - Gen Loss: -2474133.5000, Critic Loss: -19677.9336, W-Loss: -20038.1973, GP: 12.5141, Acc: 0.9037
Step  500/597 - Gen Loss: -2535366.2500, Critic Loss: -16984.1113, W-Loss: -17343.3438, GP: 12.7008, Acc: 0.9049

Generating Grid of Samples at Final Epoch 50:
Generating 6 samples per class for all 16 letter classes...

Final Generated Samples - All Letter Classes:
Generating 6 samples per class for all 16 letter classes...

# CGAN - COMPLETE IMPLEMENTATION

def build_cgan_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """
    Build CGAN generator following original CGAN paper
    Conditional generation through label embedding and concatenation
    """
    print(" Building CGAN Generator...")
    
    # Noise input
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    
    # Label input
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process label - embedding and expand
    label_embedding = tf.keras.layers.Embedding(num_classes, latent_dim)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate noise and label embedding
    combined_input = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Dense layer to create feature map
    x = tf.keras.layers.Dense(7 * 7 * 256, use_bias=False)(combined_input)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.Reshape((7, 7, 256))(x)
    
    # First upsampling: 7x7x256 -> 14x14x128
    x = tf.keras.layers.Conv2DTranspose(128, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Second upsampling: 14x14x128 -> 28x28x64
    x = tf.keras.layers.Conv2DTranspose(64, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Final layer: 28x28x64 -> 28x28x1
    x = tf.keras.layers.Conv2DTranspose(1, 5, strides=1, padding='same', activation='tanh')(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='cgan_generator'
    )
    
    return model

def build_cgan_discriminator(img_height=28, img_width=28, num_classes=16):
    """
    Build CGAN discriminator following original CGAN paper
    Joint discrimination and classification
    """
    print("Building CGAN Discriminator...")
    
    # Image input
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    
    # Label input for conditioning
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process image
    x = img_input
    
    # First conv block: 28x28x1 -> 14x14x64
    x = tf.keras.layers.Conv2D(64, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Second conv block: 14x14x64 -> 7x7x128
    x = tf.keras.layers.Conv2D(128, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Third conv block: 7x7x128 -> 4x4x256
    x = tf.keras.layers.Conv2D(256, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Flatten for dense layers
    x = tf.keras.layers.Flatten()(x)
    
    # Process label
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Concatenate image features and label
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Dense layers
    x = tf.keras.layers.Dense(1024)(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Output layer (real/fake classification)
    validity = tf.keras.layers.Dense(1, activation='sigmoid', name='validity')(x)
    
    # Auxiliary classifier for label prediction
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='cgan_discriminator'
    )
    
    return model

# Build CGAN models
cgan_generator = build_cgan_generator(
    latent_dim=100, 
    num_classes=num_classes, 
    img_height=28, 
    img_width=28
)

cgan_discriminator = build_cgan_discriminator(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes
)

 Building CGAN Generator...
Building CGAN Discriminator...

# =============================================================================
# CGAN TRAINER CLASS
# =============================================================================

class CGANTrainer:
    """CGAN trainer for standard GAN loss with auxiliary classifier"""

    def __init__(self, generator, discriminator, latent_dim=100, num_classes=16):
        self.generator = generator
        self.discriminator = discriminator
        self.latent_dim = latent_dim
        self.num_classes = num_classes

        # Optimizers
        self.gen_optimizer = tf.keras.optimizers.Adam(1e-4)
        self.disc_optimizer = tf.keras.optimizers.Adam(1e-4)

        # Loss functions
        self.bce_loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)
        self.ce_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)

        # Metrics
        self.gen_loss_metric = tf.keras.metrics.Mean(name='gen_loss')
        self.disc_loss_metric = tf.keras.metrics.Mean(name='disc_loss')
        self.acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(name='label_accuracy')

        # History
        self.history = {
            'gen_loss': [],
            'disc_loss': [],
            'label_accuracy': [],
            'epoch': []
        }

    @tf.function
    def train_step(self, real_images, real_labels):
        batch_size = tf.shape(real_images)[0]
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], minval=0, maxval=self.num_classes, dtype=tf.int32)

        # ---------------------
        # Train Discriminator
        # ---------------------
        with tf.GradientTape() as tape:
            # Generate fake images
            fake_images = self.generator([noise, fake_labels], training=True)

            # Discriminator predictions
            real_validity, real_class_logits = self.discriminator([real_images, real_labels], training=True)
            fake_validity, fake_class_logits = self.discriminator([fake_images, fake_labels], training=True)

            # Validity losses
            real_loss = self.bce_loss(tf.ones_like(real_validity), real_validity)
            fake_loss = self.bce_loss(tf.zeros_like(fake_validity), fake_validity)
            validity_loss = real_loss + fake_loss

            # Auxiliary classification loss
            real_class_loss = self.ce_loss(real_labels, real_class_logits)
            fake_class_loss = self.ce_loss(fake_labels, fake_class_logits)
            class_loss = (real_class_loss + fake_class_loss) / 2.0

            # Total discriminator loss
            disc_loss = validity_loss + class_loss

        gradients = tape.gradient(disc_loss, self.discriminator.trainable_variables)
        self.disc_optimizer.apply_gradients(zip(gradients, self.discriminator.trainable_variables))

        # Update accuracy
        self.acc_metric.update_state(real_labels, real_class_logits)

        # ---------------------
        # Train Generator
        # ---------------------
        noise = tf.random.normal([batch_size, self.latent_dim])
        gen_labels = tf.random.uniform([batch_size], minval=0, maxval=self.num_classes, dtype=tf.int32)

        with tf.GradientTape() as tape:
            generated_images = self.generator([noise, gen_labels], training=True)
            validity, class_logits = self.discriminator([generated_images, gen_labels], training=True)

            adv_loss = self.bce_loss(tf.ones_like(validity), validity)
            aux_loss = self.ce_loss(gen_labels, class_logits)
            gen_loss = adv_loss + aux_loss

        gradients = tape.gradient(gen_loss, self.generator.trainable_variables)
        self.gen_optimizer.apply_gradients(zip(gradients, self.generator.trainable_variables))

        # Update metrics
        self.gen_loss_metric.update_state(gen_loss)
        self.disc_loss_metric.update_state(disc_loss)

    def train_epoch(self, dataset, epoch, steps_per_epoch):
        print(f"\nEpoch {epoch + 1}")
        print("-" * 60)

        self.gen_loss_metric.reset_states()
        self.disc_loss_metric.reset_states()
        self.acc_metric.reset_states()

        for step, (images, labels) in enumerate(dataset.take(steps_per_epoch)):
            self.train_step(images, labels)

            if step % 100 == 0:
                print(f"Step {step:4d}/{steps_per_epoch} - "
                      f"Gen Loss: {self.gen_loss_metric.result():.4f}, "
                      f"Disc Loss: {self.disc_loss_metric.result():.4f}, "
                      f"Acc: {self.acc_metric.result():.4f}")

        # Save epoch results
        self.history['gen_loss'].append(float(self.gen_loss_metric.result()))
        self.history['disc_loss'].append(float(self.disc_loss_metric.result()))
        self.history['label_accuracy'].append(float(self.acc_metric.result()))
        self.history['epoch'].append(epoch)

# Instantiate CGAN trainer before training loop
cgan_trainer = CGANTrainer(
    generator=cgan_generator,
    discriminator=cgan_discriminator,
    latent_dim=100,
    num_classes=num_classes
)

# =============================================================================
# CGAN TRAINING - COMPLETE 50 EPOCH TRAINING
# =============================================================================

# Test visualization first
print("Testing visualization with current CGAN models...")
test_images_cgan, test_labels_cgan = display_generated_samples_grid(
    cgan_generator, class_to_letter, samples_per_class=6
)

# Training configuration
NUM_EPOCHS = 50

start_time = time.time()

for epoch in range(NUM_EPOCHS):
    epoch_start = time.time()
    
    # Train for one epoch
    cgan_trainer.train_epoch(train_dataset, epoch, steps_per_epoch)
    
    # Display only on the final epoch
    if (epoch + 1) == NUM_EPOCHS:
        print(f"\nGenerating Grid of Samples at Final Epoch {epoch + 1}:")
        display_generated_samples_grid(cgan_generator, class_to_letter, epoch + 1, samples_per_class=6)
    
    # Calculate and display epoch timing
    epoch_time = time.time() - epoch_start
    total_time = time.time() - start_time
    avg_time = total_time / (epoch + 1)
    eta = avg_time * (NUM_EPOCHS - epoch - 1)
    
total_training_time = time.time() - start_time

# Generate final display
print(f"\nFinal Generated Samples - All Letter Classes:")
final_images_cgan, final_labels_cgan = display_generated_samples_grid(
    cgan_generator, class_to_letter, NUM_EPOCHS, samples_per_class=6
)

# Plot training progress
if len(cgan_trainer.history['gen_loss']) > 1:
    plt.figure(figsize=(15, 5))
    
    # Generator and Discriminator Loss
    plt.subplot(1, 3, 1)
    epochs = cgan_trainer.history['epoch']
    plt.plot(epochs, cgan_trainer.history['gen_loss'], label='Generator Loss', color='blue', linewidth=2)
    plt.plot(epochs, cgan_trainer.history['disc_loss'], label='Discriminator Loss', color='red', linewidth=2)
    plt.title('CGAN - Training Losses', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Discriminator Accuracy
    plt.subplot(1, 3, 2)
    plt.plot(epochs, cgan_trainer.history['label_accuracy'], label='Discriminator Accuracy', color='green', linewidth=2)
    plt.axhline(y=0.95, color='red', linestyle='--', alpha=0.7, label='Upper limit (95%)')
    plt.axhline(y=0.70, color='orange', linestyle='--', alpha=0.7, label='Lower limit (70%)')
    plt.title('CGAN - Discriminator Accuracy', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Loss Difference
    plt.subplot(1, 3, 3)
    loss_diff = [g - d for g, d in zip(cgan_trainer.history['gen_loss'], cgan_trainer.history['disc_loss'])]
    plt.plot(epochs, loss_diff, label='Gen Loss - Disc Loss', color='purple', linewidth=2)
    plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)
    plt.title('CGAN - Loss Balance', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss Difference')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()


# Instantiate CGAN trainer before training loop
cgan_trainer = CGANTrainer(
    generator=cgan_generator,
    discriminator=cgan_discriminator,
    latent_dim=100,
    num_classes=num_classes
)

Testing visualization with current CGAN models...
Generating 6 samples per class for all 16 letter classes...

Epoch 1
------------------------------------------------------------
Step    0/597 - Gen Loss: 5.0598, Disc Loss: 5.6012, Acc: 0.0156
Step  100/597 - Gen Loss: 7.1038, Disc Loss: 3.5386, Acc: 0.3032
Step  200/597 - Gen Loss: 8.2649, Disc Loss: 2.9731, Acc: 0.4141
Step  300/597 - Gen Loss: 8.9221, Disc Loss: 2.7782, Acc: 0.4797
Step  400/597 - Gen Loss: 8.5059, Disc Loss: 2.5515, Acc: 0.5247
Step  500/597 - Gen Loss: 8.0641, Disc Loss: 2.3010, Acc: 0.5629

Epoch 2
------------------------------------------------------------
Step    0/597 - Gen Loss: 5.8132, Disc Loss: 0.8863, Acc: 0.7344
Step  100/597 - Gen Loss: 4.9323, Disc Loss: 0.8838, Acc: 0.7890
Step  200/597 - Gen Loss: 4.8511, Disc Loss: 0.9624, Acc: 0.8039
Step  300/597 - Gen Loss: 4.5862, Disc Loss: 0.9849, Acc: 0.8202
Step  400/597 - Gen Loss: 4.3214, Disc Loss: 0.9717, Acc: 0.8381
Step  500/597 - Gen Loss: 4.1598, Disc Loss: 0.9568, Acc: 0.8533

Epoch 3
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.9853, Disc Loss: 0.7731, Acc: 0.9688
Step  100/597 - Gen Loss: 2.8038, Disc Loss: 0.8524, Acc: 0.9503
Step  200/597 - Gen Loss: 2.6810, Disc Loss: 0.8490, Acc: 0.9580
Step  300/597 - Gen Loss: 2.6080, Disc Loss: 0.8283, Acc: 0.9621
Step  400/597 - Gen Loss: 2.5664, Disc Loss: 0.8224, Acc: 0.9655
Step  500/597 - Gen Loss: 2.5416, Disc Loss: 0.8191, Acc: 0.9681

Epoch 4
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.8358, Disc Loss: 0.7495, Acc: 1.0000
Step  100/597 - Gen Loss: 2.3346, Disc Loss: 0.8547, Acc: 0.9851
Step  200/597 - Gen Loss: 2.2474, Disc Loss: 0.8626, Acc: 0.9867
Step  300/597 - Gen Loss: 2.2162, Disc Loss: 0.8653, Acc: 0.9877
Step  400/597 - Gen Loss: 2.1724, Disc Loss: 0.8674, Acc: 0.9887
Step  500/597 - Gen Loss: 2.1381, Disc Loss: 0.8776, Acc: 0.9893

Epoch 5
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.6909, Disc Loss: 0.8477, Acc: 0.9844
Step  100/597 - Gen Loss: 1.9312, Disc Loss: 0.9085, Acc: 0.9955
Step  200/597 - Gen Loss: 1.9520, Disc Loss: 0.8846, Acc: 0.9955
Step  300/597 - Gen Loss: 1.9724, Disc Loss: 0.8648, Acc: 0.9958
Step  400/597 - Gen Loss: 2.0173, Disc Loss: 0.8302, Acc: 0.9958
Step  500/597 - Gen Loss: 2.0746, Disc Loss: 0.8100, Acc: 0.9962

Epoch 6
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.3898, Disc Loss: 0.5754, Acc: 1.0000
Step  100/597 - Gen Loss: 2.3625, Disc Loss: 0.7735, Acc: 0.9983
Step  200/597 - Gen Loss: 2.4155, Disc Loss: 0.7672, Acc: 0.9978
Step  300/597 - Gen Loss: 2.4575, Disc Loss: 0.7491, Acc: 0.9983
Step  400/597 - Gen Loss: 2.4752, Disc Loss: 0.7528, Acc: 0.9981
Step  500/597 - Gen Loss: 2.4644, Disc Loss: 0.7636, Acc: 0.9983

Epoch 7
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7608, Disc Loss: 0.7054, Acc: 1.0000
Step  100/597 - Gen Loss: 2.1904, Disc Loss: 0.8092, Acc: 0.9994
Step  200/597 - Gen Loss: 2.1649, Disc Loss: 0.7810, Acc: 0.9992
Step  300/597 - Gen Loss: 2.1906, Disc Loss: 0.7829, Acc: 0.9992
Step  400/597 - Gen Loss: 2.1817, Disc Loss: 0.7955, Acc: 0.9992
Step  500/597 - Gen Loss: 2.1735, Disc Loss: 0.8008, Acc: 0.9992

Epoch 8
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.1570, Disc Loss: 1.2238, Acc: 1.0000
Step  100/597 - Gen Loss: 2.1324, Disc Loss: 0.8146, Acc: 0.9995
Step  200/597 - Gen Loss: 2.1263, Disc Loss: 0.8124, Acc: 0.9997
Step  300/597 - Gen Loss: 2.1203, Disc Loss: 0.8132, Acc: 0.9994
Step  400/597 - Gen Loss: 2.1138, Disc Loss: 0.8205, Acc: 0.9993
Step  500/597 - Gen Loss: 2.1246, Disc Loss: 0.8058, Acc: 0.9994

Epoch 9
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.9921, Disc Loss: 0.9112, Acc: 1.0000
Step  100/597 - Gen Loss: 2.1556, Disc Loss: 0.8405, Acc: 0.9995
Step  200/597 - Gen Loss: 2.1151, Disc Loss: 0.8227, Acc: 0.9995
Step  300/597 - Gen Loss: 2.0856, Disc Loss: 0.8267, Acc: 0.9996
Step  400/597 - Gen Loss: 2.0959, Disc Loss: 0.8326, Acc: 0.9996
Step  500/597 - Gen Loss: 2.0823, Disc Loss: 0.8328, Acc: 0.9996

Epoch 10
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.6363, Disc Loss: 1.1065, Acc: 1.0000
Step  100/597 - Gen Loss: 2.0544, Disc Loss: 0.8455, Acc: 0.9998
Step  200/597 - Gen Loss: 2.0135, Disc Loss: 0.8574, Acc: 0.9998
Step  300/597 - Gen Loss: 2.0266, Disc Loss: 0.8414, Acc: 0.9998
Step  400/597 - Gen Loss: 2.0270, Disc Loss: 0.8512, Acc: 0.9998
Step  500/597 - Gen Loss: 2.0100, Disc Loss: 0.8498, Acc: 0.9998

Epoch 11
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.4414, Disc Loss: 0.9911, Acc: 1.0000
Step  100/597 - Gen Loss: 1.9453, Disc Loss: 0.8387, Acc: 0.9998
Step  200/597 - Gen Loss: 1.9198, Disc Loss: 0.8522, Acc: 0.9998
Step  300/597 - Gen Loss: 1.9053, Disc Loss: 0.8756, Acc: 0.9998
Step  400/597 - Gen Loss: 1.9128, Disc Loss: 0.8804, Acc: 0.9998
Step  500/597 - Gen Loss: 1.8853, Disc Loss: 0.8885, Acc: 0.9999

Epoch 12
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.5934, Disc Loss: 0.6902, Acc: 1.0000
Step  100/597 - Gen Loss: 1.8064, Disc Loss: 0.9090, Acc: 0.9998
Step  200/597 - Gen Loss: 1.7954, Disc Loss: 0.9177, Acc: 0.9999
Step  300/597 - Gen Loss: 1.8078, Disc Loss: 0.9171, Acc: 0.9999
Step  400/597 - Gen Loss: 1.7997, Disc Loss: 0.9176, Acc: 0.9999
Step  500/597 - Gen Loss: 1.7845, Disc Loss: 0.9153, Acc: 0.9998

Epoch 13
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0378, Disc Loss: 0.7232, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7785, Disc Loss: 0.9370, Acc: 0.9997
Step  200/597 - Gen Loss: 1.7601, Disc Loss: 0.9278, Acc: 0.9998
Step  300/597 - Gen Loss: 1.7421, Disc Loss: 0.9281, Acc: 0.9998
Step  400/597 - Gen Loss: 1.7518, Disc Loss: 0.9290, Acc: 0.9999
Step  500/597 - Gen Loss: 1.7517, Disc Loss: 0.9341, Acc: 0.9999

Epoch 14
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1977, Disc Loss: 0.8241, Acc: 1.0000
Step  100/597 - Gen Loss: 1.6852, Disc Loss: 0.9497, Acc: 0.9998
Step  200/597 - Gen Loss: 1.6897, Disc Loss: 0.9255, Acc: 0.9999
Step  300/597 - Gen Loss: 1.7218, Disc Loss: 0.9219, Acc: 0.9999
Step  400/597 - Gen Loss: 1.7330, Disc Loss: 0.9273, Acc: 0.9998
Step  500/597 - Gen Loss: 1.7309, Disc Loss: 0.9336, Acc: 0.9998

Epoch 15
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3696, Disc Loss: 0.7744, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7507, Disc Loss: 0.9422, Acc: 0.9998
Step  200/597 - Gen Loss: 1.7196, Disc Loss: 0.9526, Acc: 0.9999
Step  300/597 - Gen Loss: 1.7010, Disc Loss: 0.9538, Acc: 0.9999
Step  400/597 - Gen Loss: 1.6872, Disc Loss: 0.9557, Acc: 0.9999
Step  500/597 - Gen Loss: 1.6914, Disc Loss: 0.9571, Acc: 0.9999

Epoch 16
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.6109, Disc Loss: 1.1583, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7017, Disc Loss: 0.9604, Acc: 0.9998
Step  200/597 - Gen Loss: 1.7061, Disc Loss: 0.9564, Acc: 0.9998
Step  300/597 - Gen Loss: 1.6871, Disc Loss: 0.9593, Acc: 0.9998
Step  400/597 - Gen Loss: 1.6920, Disc Loss: 0.9499, Acc: 0.9999
Step  500/597 - Gen Loss: 1.6834, Disc Loss: 0.9550, Acc: 0.9999

Epoch 17
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3723, Disc Loss: 0.7417, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5921, Disc Loss: 0.9806, Acc: 1.0000
Step  200/597 - Gen Loss: 1.6177, Disc Loss: 0.9761, Acc: 1.0000
Step  300/597 - Gen Loss: 1.6230, Disc Loss: 0.9745, Acc: 1.0000
Step  400/597 - Gen Loss: 1.6374, Disc Loss: 0.9661, Acc: 0.9999
Step  500/597 - Gen Loss: 1.6426, Disc Loss: 0.9615, Acc: 0.9999

Epoch 18
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.8726, Disc Loss: 1.0343, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5486, Disc Loss: 0.9698, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5739, Disc Loss: 0.9807, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5685, Disc Loss: 0.9723, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5745, Disc Loss: 0.9718, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5804, Disc Loss: 0.9656, Acc: 1.0000

Epoch 19
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.1206, Disc Loss: 0.9742, Acc: 1.0000
Step  100/597 - Gen Loss: 1.6684, Disc Loss: 0.9933, Acc: 1.0000
Step  200/597 - Gen Loss: 1.6552, Disc Loss: 0.9777, Acc: 1.0000
Step  300/597 - Gen Loss: 1.6360, Disc Loss: 0.9693, Acc: 1.0000
Step  400/597 - Gen Loss: 1.6339, Disc Loss: 0.9661, Acc: 1.0000
Step  500/597 - Gen Loss: 1.6218, Disc Loss: 0.9659, Acc: 1.0000

Epoch 20
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.9822, Disc Loss: 0.9079, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5733, Disc Loss: 1.0006, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5603, Disc Loss: 0.9922, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5838, Disc Loss: 0.9789, Acc: 1.0000
Step  400/597 - Gen Loss: 1.6020, Disc Loss: 0.9673, Acc: 1.0000
Step  500/597 - Gen Loss: 1.6050, Disc Loss: 0.9677, Acc: 1.0000

Epoch 21
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.6322, Disc Loss: 0.7747, Acc: 1.0000
Step  100/597 - Gen Loss: 1.6354, Disc Loss: 0.9954, Acc: 1.0000
Step  200/597 - Gen Loss: 1.6006, Disc Loss: 0.9921, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5870, Disc Loss: 1.0033, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5722, Disc Loss: 0.9983, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5603, Disc Loss: 0.9937, Acc: 1.0000

Epoch 22
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.8528, Disc Loss: 0.9226, Acc: 1.0000
Step  100/597 - Gen Loss: 1.4967, Disc Loss: 1.0180, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5102, Disc Loss: 1.0120, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5346, Disc Loss: 0.9920, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5412, Disc Loss: 0.9982, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5354, Disc Loss: 0.9980, Acc: 1.0000

Epoch 23
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.4286, Disc Loss: 0.8563, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5528, Disc Loss: 0.9853, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5578, Disc Loss: 0.9776, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5650, Disc Loss: 0.9724, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5747, Disc Loss: 0.9810, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5506, Disc Loss: 0.9923, Acc: 1.0000

Epoch 24
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.4679, Disc Loss: 0.9005, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5223, Disc Loss: 0.9916, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5264, Disc Loss: 1.0003, Acc: 0.9999
Step  300/597 - Gen Loss: 1.5114, Disc Loss: 0.9940, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5274, Disc Loss: 0.9903, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5351, Disc Loss: 0.9927, Acc: 1.0000

Epoch 25
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.2026, Disc Loss: 0.9910, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5138, Disc Loss: 1.0031, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5116, Disc Loss: 1.0039, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5214, Disc Loss: 0.9927, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5404, Disc Loss: 0.9849, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5401, Disc Loss: 0.9852, Acc: 1.0000

Epoch 26
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0801, Disc Loss: 1.1843, Acc: 1.0000
Step  100/597 - Gen Loss: 1.4476, Disc Loss: 1.0159, Acc: 1.0000
Step  200/597 - Gen Loss: 1.4513, Disc Loss: 1.0099, Acc: 1.0000
Step  300/597 - Gen Loss: 1.4622, Disc Loss: 0.9985, Acc: 1.0000
Step  400/597 - Gen Loss: 1.4740, Disc Loss: 0.9995, Acc: 1.0000
Step  500/597 - Gen Loss: 1.4961, Disc Loss: 0.9953, Acc: 1.0000

Epoch 27
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1296, Disc Loss: 0.6856, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5359, Disc Loss: 0.9997, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5140, Disc Loss: 0.9918, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5326, Disc Loss: 0.9884, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5417, Disc Loss: 0.9846, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5387, Disc Loss: 0.9946, Acc: 1.0000

Epoch 28
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3007, Disc Loss: 1.0598, Acc: 1.0000
Step  100/597 - Gen Loss: 1.4465, Disc Loss: 1.0062, Acc: 0.9998
Step  200/597 - Gen Loss: 1.4765, Disc Loss: 1.0059, Acc: 0.9999
Step  300/597 - Gen Loss: 1.4908, Disc Loss: 1.0081, Acc: 0.9999
Step  400/597 - Gen Loss: 1.4979, Disc Loss: 0.9945, Acc: 0.9999
Step  500/597 - Gen Loss: 1.5041, Disc Loss: 1.0024, Acc: 0.9999

Epoch 29
------------------------------------------------------------
Step    0/597 - Gen Loss: 0.9484, Disc Loss: 1.3155, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5220, Disc Loss: 0.9905, Acc: 0.9998
Step  200/597 - Gen Loss: 1.5098, Disc Loss: 0.9795, Acc: 0.9999
Step  300/597 - Gen Loss: 1.5227, Disc Loss: 0.9765, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5352, Disc Loss: 0.9702, Acc: 0.9999
Step  500/597 - Gen Loss: 1.5328, Disc Loss: 0.9781, Acc: 0.9999

Epoch 30
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.5309, Disc Loss: 0.8102, Acc: 1.0000
Step  100/597 - Gen Loss: 1.6077, Disc Loss: 0.9597, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5573, Disc Loss: 0.9951, Acc: 0.9999
Step  300/597 - Gen Loss: 1.5474, Disc Loss: 1.0031, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5521, Disc Loss: 0.9992, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5461, Disc Loss: 0.9996, Acc: 0.9999

Epoch 31
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0427, Disc Loss: 0.7163, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5034, Disc Loss: 0.9870, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5642, Disc Loss: 0.9876, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5366, Disc Loss: 0.9899, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5163, Disc Loss: 0.9922, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5180, Disc Loss: 0.9899, Acc: 1.0000

Epoch 32
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.5243, Disc Loss: 1.0807, Acc: 1.0000
Step  100/597 - Gen Loss: 1.4988, Disc Loss: 1.0195, Acc: 1.0000
Step  200/597 - Gen Loss: 1.4719, Disc Loss: 1.0246, Acc: 1.0000
Step  300/597 - Gen Loss: 1.4759, Disc Loss: 1.0146, Acc: 1.0000
Step  400/597 - Gen Loss: 1.4857, Disc Loss: 1.0043, Acc: 1.0000
Step  500/597 - Gen Loss: 1.4937, Disc Loss: 0.9921, Acc: 1.0000

Epoch 33
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1048, Disc Loss: 0.9096, Acc: 1.0000
Step  100/597 - Gen Loss: 1.4984, Disc Loss: 0.9752, Acc: 0.9998
Step  200/597 - Gen Loss: 1.5341, Disc Loss: 0.9853, Acc: 0.9999
Step  300/597 - Gen Loss: 1.5421, Disc Loss: 0.9743, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5576, Disc Loss: 0.9694, Acc: 0.9999
Step  500/597 - Gen Loss: 1.5514, Disc Loss: 0.9768, Acc: 0.9999

Epoch 34
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.3499, Disc Loss: 0.8546, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5342, Disc Loss: 1.0657, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5402, Disc Loss: 1.0137, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5392, Disc Loss: 0.9921, Acc: 0.9999
Step  400/597 - Gen Loss: 1.5426, Disc Loss: 0.9714, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5597, Disc Loss: 0.9764, Acc: 1.0000

Epoch 35
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1955, Disc Loss: 1.1284, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5625, Disc Loss: 1.0004, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5398, Disc Loss: 0.9904, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5538, Disc Loss: 0.9789, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5649, Disc Loss: 0.9742, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5761, Disc Loss: 0.9695, Acc: 1.0000

Epoch 36
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.6861, Disc Loss: 1.0736, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5386, Disc Loss: 1.0138, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5441, Disc Loss: 0.9887, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5504, Disc Loss: 0.9932, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5577, Disc Loss: 0.9743, Acc: 1.0000
Step  500/597 - Gen Loss: 1.5815, Disc Loss: 0.9705, Acc: 1.0000

Epoch 37
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3817, Disc Loss: 0.7744, Acc: 1.0000
Step  100/597 - Gen Loss: 1.5922, Disc Loss: 0.9536, Acc: 1.0000
Step  200/597 - Gen Loss: 1.5994, Disc Loss: 0.9640, Acc: 1.0000
Step  300/597 - Gen Loss: 1.5856, Disc Loss: 0.9547, Acc: 1.0000
Step  400/597 - Gen Loss: 1.5917, Disc Loss: 0.9442, Acc: 1.0000
Step  500/597 - Gen Loss: 1.6120, Disc Loss: 0.9535, Acc: 1.0000

Epoch 38
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.5896, Disc Loss: 0.9153, Acc: 1.0000
Step  100/597 - Gen Loss: 1.6128, Disc Loss: 0.9406, Acc: 1.0000
Step  200/597 - Gen Loss: 1.6142, Disc Loss: 0.9533, Acc: 1.0000
Step  300/597 - Gen Loss: 1.6488, Disc Loss: 0.9497, Acc: 1.0000
Step  400/597 - Gen Loss: 1.6636, Disc Loss: 0.9520, Acc: 1.0000
Step  500/597 - Gen Loss: 1.6630, Disc Loss: 0.9580, Acc: 1.0000

Epoch 39
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0271, Disc Loss: 0.8042, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7471, Disc Loss: 0.8999, Acc: 1.0000
Step  200/597 - Gen Loss: 1.7032, Disc Loss: 0.9289, Acc: 1.0000
Step  300/597 - Gen Loss: 1.6981, Disc Loss: 0.9281, Acc: 1.0000
Step  400/597 - Gen Loss: 1.7241, Disc Loss: 0.9256, Acc: 1.0000
Step  500/597 - Gen Loss: 1.7191, Disc Loss: 0.9342, Acc: 1.0000

Epoch 40
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3179, Disc Loss: 0.8334, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7622, Disc Loss: 0.8713, Acc: 1.0000
Step  200/597 - Gen Loss: 1.8087, Disc Loss: 0.9049, Acc: 1.0000
Step  300/597 - Gen Loss: 1.8252, Disc Loss: 0.9030, Acc: 0.9999
Step  400/597 - Gen Loss: 1.8056, Disc Loss: 0.9081, Acc: 1.0000
Step  500/597 - Gen Loss: 1.7988, Disc Loss: 0.9066, Acc: 1.0000

Epoch 41
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0459, Disc Loss: 1.0854, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7037, Disc Loss: 0.9490, Acc: 1.0000
Step  200/597 - Gen Loss: 1.7028, Disc Loss: 0.9570, Acc: 1.0000
Step  300/597 - Gen Loss: 1.6937, Disc Loss: 0.9611, Acc: 1.0000
Step  400/597 - Gen Loss: 1.7075, Disc Loss: 0.9491, Acc: 1.0000
Step  500/597 - Gen Loss: 1.7204, Disc Loss: 0.9540, Acc: 1.0000

Epoch 42
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.2901, Disc Loss: 0.8918, Acc: 1.0000
Step  100/597 - Gen Loss: 1.7661, Disc Loss: 0.9183, Acc: 0.9998
Step  200/597 - Gen Loss: 1.7554, Disc Loss: 0.9054, Acc: 0.9999
Step  300/597 - Gen Loss: 1.7868, Disc Loss: 0.8946, Acc: 0.9999
Step  400/597 - Gen Loss: 1.7972, Disc Loss: 0.8886, Acc: 1.0000
Step  500/597 - Gen Loss: 1.7653, Disc Loss: 0.9166, Acc: 1.0000

Epoch 43
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.8830, Disc Loss: 1.4287, Acc: 1.0000
Step  100/597 - Gen Loss: 1.8709, Disc Loss: 0.8817, Acc: 1.0000
Step  200/597 - Gen Loss: 1.8296, Disc Loss: 0.9213, Acc: 1.0000
Step  300/597 - Gen Loss: 1.7817, Disc Loss: 0.9033, Acc: 1.0000
Step  400/597 - Gen Loss: 1.7889, Disc Loss: 0.8976, Acc: 1.0000
Step  500/597 - Gen Loss: 1.8012, Disc Loss: 0.8948, Acc: 1.0000

Epoch 44
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.2190, Disc Loss: 0.8221, Acc: 1.0000
Step  100/597 - Gen Loss: 1.6523, Disc Loss: 0.9457, Acc: 0.9998
Step  200/597 - Gen Loss: 1.6693, Disc Loss: 0.9236, Acc: 0.9999
Step  300/597 - Gen Loss: 1.6994, Disc Loss: 0.9208, Acc: 0.9999
Step  400/597 - Gen Loss: 1.7166, Disc Loss: 0.9094, Acc: 1.0000
Step  500/597 - Gen Loss: 1.7305, Disc Loss: 0.9080, Acc: 1.0000

Epoch 45
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7449, Disc Loss: 0.3622, Acc: 1.0000
Step  100/597 - Gen Loss: 1.8344, Disc Loss: 0.8806, Acc: 1.0000
Step  200/597 - Gen Loss: 1.8582, Disc Loss: 0.8833, Acc: 1.0000
Step  300/597 - Gen Loss: 1.8269, Disc Loss: 0.8788, Acc: 1.0000
Step  400/597 - Gen Loss: 1.8347, Disc Loss: 0.8806, Acc: 1.0000
Step  500/597 - Gen Loss: 1.8798, Disc Loss: 0.8577, Acc: 1.0000

Epoch 46
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7991, Disc Loss: 1.1140, Acc: 1.0000
Step  100/597 - Gen Loss: 1.8458, Disc Loss: 0.9426, Acc: 1.0000
Step  200/597 - Gen Loss: 1.8739, Disc Loss: 0.8929, Acc: 1.0000
Step  300/597 - Gen Loss: 1.8811, Disc Loss: 0.8685, Acc: 1.0000
Step  400/597 - Gen Loss: 1.8846, Disc Loss: 0.8838, Acc: 1.0000
Step  500/597 - Gen Loss: 1.8523, Disc Loss: 0.8937, Acc: 1.0000

Epoch 47
------------------------------------------------------------
Step    0/597 - Gen Loss: 0.8511, Disc Loss: 0.9797, Acc: 1.0000
Step  100/597 - Gen Loss: 1.9912, Disc Loss: 0.8249, Acc: 1.0000
Step  200/597 - Gen Loss: 1.9348, Disc Loss: 0.8494, Acc: 1.0000
Step  300/597 - Gen Loss: 1.9125, Disc Loss: 0.8593, Acc: 1.0000
Step  400/597 - Gen Loss: 1.9001, Disc Loss: 0.8672, Acc: 1.0000
Step  500/597 - Gen Loss: 1.8875, Disc Loss: 0.8750, Acc: 1.0000

Epoch 48
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.7912, Disc Loss: 0.7600, Acc: 1.0000
Step  100/597 - Gen Loss: 1.8537, Disc Loss: 0.8658, Acc: 1.0000
Step  200/597 - Gen Loss: 1.9122, Disc Loss: 0.8806, Acc: 1.0000
Step  300/597 - Gen Loss: 1.8679, Disc Loss: 0.8863, Acc: 1.0000
Step  400/597 - Gen Loss: 1.8963, Disc Loss: 0.8600, Acc: 1.0000
Step  500/597 - Gen Loss: 1.8973, Disc Loss: 0.8810, Acc: 1.0000

Epoch 49
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.5502, Disc Loss: 0.6530, Acc: 1.0000
Step  100/597 - Gen Loss: 2.0059, Disc Loss: 0.7974, Acc: 1.0000
Step  200/597 - Gen Loss: 1.9342, Disc Loss: 0.8670, Acc: 1.0000
Step  300/597 - Gen Loss: 1.8962, Disc Loss: 0.8771, Acc: 1.0000
Step  400/597 - Gen Loss: 1.8617, Disc Loss: 0.8795, Acc: 1.0000
Step  500/597 - Gen Loss: 1.8400, Disc Loss: 0.8835, Acc: 1.0000

Epoch 50
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.0670, Disc Loss: 0.9121, Acc: 1.0000
Step  100/597 - Gen Loss: 1.9531, Disc Loss: 0.8352, Acc: 1.0000
Step  200/597 - Gen Loss: 1.9198, Disc Loss: 0.8753, Acc: 1.0000
Step  300/597 - Gen Loss: 1.9161, Disc Loss: 0.8558, Acc: 1.0000
Step  400/597 - Gen Loss: 1.8767, Disc Loss: 0.8702, Acc: 1.0000
Step  500/597 - Gen Loss: 1.9094, Disc Loss: 0.8628, Acc: 1.0000

Generating Grid of Samples at Final Epoch 50:
Generating 6 samples per class for all 16 letter classes...

Final Generated Samples - All Letter Classes:
Generating 6 samples per class for all 16 letter classes...

# ENHANCED CGAN - COMPLETE IMPLEMENTATION

class SpectralNormalization(tf.keras.layers.Wrapper):
    """Spectral Normalization wrapper for improved training stability"""
    
    def __init__(self, layer, **kwargs):
        super(SpectralNormalization, self).__init__(layer, **kwargs)
        
    def build(self, input_shape):
        self.layer.build(input_shape)
        self.w = self.layer.kernel
        self.w_shape = self.w.shape.as_list()
        
        self.u = self.add_weight(
            shape=(1, self.w_shape[-1]),
            initializer='random_normal',
            trainable=False,
            name='u'
        )
        
        super(SpectralNormalization, self).build()
        
    def call(self, inputs, training=None):
        self.update_weights()
        return self.layer(inputs)
        
    def update_weights(self):
        w_reshaped = tf.reshape(self.w, [-1, self.w_shape[-1]])
        
        u_hat = self.u
        v_hat = tf.nn.l2_normalize(tf.matmul(u_hat, w_reshaped, transpose_b=True))
        u_hat = tf.nn.l2_normalize(tf.matmul(v_hat, w_reshaped))
        
        sigma = tf.matmul(tf.matmul(v_hat, w_reshaped), u_hat, transpose_b=True)
        self.w.assign(self.w / sigma)
        self.u.assign(u_hat)

class SelfAttention(tf.keras.layers.Layer):
    """Self-attention mechanism for better spatial coherence"""
    
    def __init__(self, **kwargs):
        super(SelfAttention, self).__init__(**kwargs)
        
    def build(self, input_shape):
        # Convert TensorShape to int
        channels = int(input_shape[-1])
        
        self.query_conv = tf.keras.layers.Conv2D(channels // 8, 1)
        self.key_conv = tf.keras.layers.Conv2D(channels // 8, 1)
        self.value_conv = tf.keras.layers.Conv2D(channels, 1)
        self.gamma = self.add_weight(
            shape=(),
            initializer='zeros',
            trainable=True,
            name='gamma'
        )
        super(SelfAttention, self).build(input_shape)
        
    def call(self, inputs):
        batch_size, height, width, channels = tf.shape(inputs)[0], tf.shape(inputs)[1], tf.shape(inputs)[2], tf.shape(inputs)[3]
        
        # Generate query, key, value
        query = self.query_conv(inputs)
        key = self.key_conv(inputs)
        value = self.value_conv(inputs)
        
        # Reshape for attention computation
        query = tf.reshape(query, [batch_size, -1, tf.shape(query)[-1]])
        key = tf.reshape(key, [batch_size, -1, tf.shape(key)[-1]])
        value = tf.reshape(value, [batch_size, -1, tf.shape(value)[-1]])
        
        # Compute attention
        attention = tf.nn.softmax(tf.matmul(query, key, transpose_b=True))
        out = tf.matmul(attention, value)
        
        # Reshape back
        out = tf.reshape(out, [batch_size, height, width, channels])
        
        return self.gamma * out + inputs

class FiLM(tf.keras.layers.Layer):
    """Feature-wise Linear Modulation for better conditional generation"""
    
    def __init__(self, num_classes, **kwargs):
        super(FiLM, self).__init__(**kwargs)
        self.num_classes = num_classes
        
    def build(self, input_shape):
        # Convert TensorShape to int
        self.channels = int(input_shape[-1])
        
        self.gamma_dense = tf.keras.layers.Dense(self.channels)
        self.beta_dense = tf.keras.layers.Dense(self.channels)
        self.label_embedding = tf.keras.layers.Embedding(self.num_classes, 128)
        super(FiLM, self).build(input_shape)
        
    def call(self, inputs, labels):
        # inputs: [batch, height, width, channels]
        # labels: [batch]
        
        # Generate conditioning parameters
        label_emb = self.label_embedding(labels)
        gamma = self.gamma_dense(label_emb)
        beta = self.beta_dense(label_emb)
        
        # Reshape for broadcasting
        gamma = tf.expand_dims(tf.expand_dims(gamma, 1), 1)
        beta = tf.expand_dims(tf.expand_dims(beta, 1), 1)
        
        # Apply FiLM
        return gamma * inputs + beta

def build_enhanced_cgan_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """Enhanced CGAN generator with advanced features"""
    print("🔨 Building Enhanced CGAN Generator...")
    
    # Inputs
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Enhanced label conditioning
    label_embedding = tf.keras.layers.Embedding(num_classes, latent_dim)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Combine noise and label
    combined = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Dense layer with residual connection
    x = tf.keras.layers.Dense(7 * 7 * 512, use_bias=False)(combined)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.Reshape((7, 7, 512))(x)
    
    # First upsampling block with FiLM conditioning
    x = tf.keras.layers.Conv2DTranspose(256, 4, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    
    # Apply FiLM conditioning
    film_layer_1 = FiLM(num_classes)
    x = film_layer_1(x, label_input)
    x = tf.keras.layers.ReLU()(x)
    
    # Self-attention for better spatial coherence
    x = SelfAttention()(x)
    
    # Second upsampling block
    x = tf.keras.layers.Conv2DTranspose(128, 4, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    
    # Apply FiLM conditioning
    film_layer_2 = FiLM(num_classes)
    x = film_layer_2(x, label_input)
    x = tf.keras.layers.ReLU()(x)
    
    # Final output layer
    x = tf.keras.layers.Conv2D(1, 3, padding='same', activation='tanh')(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='enhanced_cgan_generator'
    )
    
    return model

def build_enhanced_cgan_discriminator(img_height=28, img_width=28, num_classes=16):
    """Enhanced CGAN discriminator with spectral normalization"""

    # Inputs
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process image with spectral normalization
    x = img_input
    
    # First conv block with spectral norm
    x = SpectralNormalization(tf.keras.layers.Conv2D(64, 4, strides=2, padding='same'))(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Second conv block
    x = SpectralNormalization(tf.keras.layers.Conv2D(128, 4, strides=2, padding='same'))(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Third conv block with self-attention
    x = SpectralNormalization(tf.keras.layers.Conv2D(256, 4, strides=2, padding='same'))(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = SelfAttention()(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Fourth conv block
    x = SpectralNormalization(tf.keras.layers.Conv2D(512, 4, strides=2, padding='same'))(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.4)(x)
    
    # Flatten and process label
    x = tf.keras.layers.Flatten()(x)
    
    # Enhanced label processing
    label_embedding = tf.keras.layers.Embedding(num_classes, 128)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Combine features
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Dense layers with spectral norm
    x = SpectralNormalization(tf.keras.layers.Dense(1024))(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    x = SpectralNormalization(tf.keras.layers.Dense(512))(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Output layers
    validity = tf.keras.layers.Dense(1, activation='sigmoid', name='validity')(x)
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='enhanced_cgan_discriminator'
    )
    
    return model

# Build Enhanced CGAN models
enhanced_cgan_generator = build_enhanced_cgan_generator(
    latent_dim=100, 
    num_classes=num_classes, 
    img_height=28, 
    img_width=28
)

enhanced_cgan_discriminator = build_enhanced_cgan_discriminator(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes
)

# Display model architectures
print("\nEnhanced CGAN Generator Architecture:")
enhanced_cgan_generator.summary()

print("\nEnhanced CGAN Discriminator Architecture:")
enhanced_cgan_discriminator.summary()

🔨 Building Enhanced CGAN Generator...

Enhanced CGAN Generator Architecture:
Model: "enhanced_cgan_generator"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 label_input (InputLayer)       [(None,)]            0           []                               
                                                                                                  
 embedding_14 (Embedding)       (None, 100)          1600        ['label_input[0][0]']            
                                                                                                  
 noise_input (InputLayer)       [(None, 100)]        0           []                               
                                                                                                  
 flatten_22 (Flatten)           (None, 100)          0           ['embedding_14[0][0]']           
                                                                                                  
 concatenate_16 (Concatenate)   (None, 200)          0           ['noise_input[0][0]',            
                                                                  'flatten_22[0][0]']             
                                                                                                  
 dense_17 (Dense)               (None, 25088)        5017600     ['concatenate_16[0][0]']         
                                                                                                  
 batch_normalization_134 (Batch  (None, 25088)       100352      ['dense_17[0][0]']               
 Normalization)                                                                                   
                                                                                                  
 re_lu_17 (ReLU)                (None, 25088)        0           ['batch_normalization_134[0][0]']
                                                                                                  
 reshape_9 (Reshape)            (None, 7, 7, 512)    0           ['re_lu_17[0][0]']               
                                                                                                  
 conv2d_transpose_24 (Conv2DTra  (None, 14, 14, 256)  2097152    ['reshape_9[0][0]']              
 nspose)                                                                                          
                                                                                                  
 batch_normalization_135 (Batch  (None, 14, 14, 256)  1024       ['conv2d_transpose_24[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 fi_lm (FiLM)                   (None, 14, 14, 256)  68096       ['batch_normalization_135[0][0]',
                                                                  'label_input[0][0]']            
                                                                                                  
 re_lu_18 (ReLU)                (None, 14, 14, 256)  0           ['fi_lm[0][0]']                  
                                                                                                  
 self_attention (SelfAttention)  (None, 14, 14, 256)  82241      ['re_lu_18[0][0]']               
                                                                                                  
 conv2d_transpose_25 (Conv2DTra  (None, 28, 28, 128)  524288     ['self_attention[0][0]']         
 nspose)                                                                                          
                                                                                                  
 batch_normalization_136 (Batch  (None, 28, 28, 128)  512        ['conv2d_transpose_25[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 fi_lm_1 (FiLM)                 (None, 28, 28, 128)  35072       ['batch_normalization_136[0][0]',
                                                                  'label_input[0][0]']            
                                                                                                  
 re_lu_19 (ReLU)                (None, 28, 28, 128)  0           ['fi_lm_1[0][0]']                
                                                                                                  
 conv2d_129 (Conv2D)            (None, 28, 28, 1)    1153        ['re_lu_19[0][0]']               
                                                                                                  
==================================================================================================
Total params: 7,929,090
Trainable params: 7,878,146
Non-trainable params: 50,944
__________________________________________________________________________________________________

Enhanced CGAN Discriminator Architecture:
Model: "enhanced_cgan_discriminator"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 img_input (InputLayer)         [(None, 28, 28, 1)]  0           []                               
                                                                                                  
 spectral_normalization_3 (Spec  (None, 14, 14, 64)  1152        ['img_input[0][0]']              
 tralNormalization)                                                                               
                                                                                                  
 leaky_re_lu_36 (LeakyReLU)     (None, 14, 14, 64)   0           ['spectral_normalization_3[0][0]'
                                                                 ]                                
                                                                                                  
 dropout_26 (Dropout)           (None, 14, 14, 64)   0           ['leaky_re_lu_36[0][0]']         
                                                                                                  
 spectral_normalization_4 (Spec  (None, 7, 7, 128)   131328      ['dropout_26[0][0]']             
 tralNormalization)                                                                               
                                                                                                  
 batch_normalization_137 (Batch  (None, 7, 7, 128)   512         ['spectral_normalization_4[0][0]'
 Normalization)                                                  ]                                
                                                                                                  
 leaky_re_lu_37 (LeakyReLU)     (None, 7, 7, 128)    0           ['batch_normalization_137[0][0]']
                                                                                                  
 dropout_27 (Dropout)           (None, 7, 7, 128)    0           ['leaky_re_lu_37[0][0]']         
                                                                                                  
 spectral_normalization_5 (Spec  (None, 4, 4, 256)   524800      ['dropout_27[0][0]']             
 tralNormalization)                                                                               
                                                                                                  
 batch_normalization_138 (Batch  (None, 4, 4, 256)   1024        ['spectral_normalization_5[0][0]'
 Normalization)                                                  ]                                
                                                                                                  
 leaky_re_lu_38 (LeakyReLU)     (None, 4, 4, 256)    0           ['batch_normalization_138[0][0]']
                                                                                                  
 self_attention_1 (SelfAttentio  (None, 4, 4, 256)   82241       ['leaky_re_lu_38[0][0]']         
 n)                                                                                               
                                                                                                  
 dropout_28 (Dropout)           (None, 4, 4, 256)    0           ['self_attention_1[0][0]']       
                                                                                                  
 spectral_normalization_6 (Spec  (None, 2, 2, 512)   2098176     ['dropout_28[0][0]']             
 tralNormalization)                                                                               
                                                                                                  
 batch_normalization_139 (Batch  (None, 2, 2, 512)   2048        ['spectral_normalization_6[0][0]'
 Normalization)                                                  ]                                
                                                                                                  
 leaky_re_lu_39 (LeakyReLU)     (None, 2, 2, 512)    0           ['batch_normalization_139[0][0]']
                                                                                                  
 label_input (InputLayer)       [(None,)]            0           []                               
                                                                                                  
 dropout_29 (Dropout)           (None, 2, 2, 512)    0           ['leaky_re_lu_39[0][0]']         
                                                                                                  
 embedding_15 (Embedding)       (None, 128)          2048        ['label_input[0][0]']            
                                                                                                  
 flatten_23 (Flatten)           (None, 2048)         0           ['dropout_29[0][0]']             
                                                                                                  
 flatten_24 (Flatten)           (None, 128)          0           ['embedding_15[0][0]']           
                                                                                                  
 concatenate_17 (Concatenate)   (None, 2176)         0           ['flatten_23[0][0]',             
                                                                  'flatten_24[0][0]']             
                                                                                                  
 spectral_normalization_7 (Spec  (None, 1024)        2230272     ['concatenate_17[0][0]']         
 tralNormalization)                                                                               
                                                                                                  
 leaky_re_lu_40 (LeakyReLU)     (None, 1024)         0           ['spectral_normalization_7[0][0]'
                                                                 ]                                
                                                                                                  
 dropout_30 (Dropout)           (None, 1024)         0           ['leaky_re_lu_40[0][0]']         
                                                                                                  
 spectral_normalization_8 (Spec  (None, 512)         525312      ['dropout_30[0][0]']             
 tralNormalization)                                                                               
                                                                                                  
 leaky_re_lu_41 (LeakyReLU)     (None, 512)          0           ['spectral_normalization_8[0][0]'
                                                                 ]                                
                                                                                                  
 dropout_31 (Dropout)           (None, 512)          0           ['leaky_re_lu_41[0][0]']         
                                                                                                  
 validity (Dense)               (None, 1)            513         ['dropout_31[0][0]']             
                                                                                                  
 label_pred (Dense)             (None, 16)           8208        ['dropout_31[0][0]']             
                                                                                                  
==================================================================================================
Total params: 5,607,634
Trainable params: 5,603,346
Non-trainable params: 4,288
__________________________________________________________________________________________________

# IMPROVED ENHANCED CGAN MODELS - BETTER ARCHITECTURE

# Custom layers for better performance
class SpectralNormalization(tf.keras.layers.Wrapper):
    def __init__(self, layer, **kwargs):
        super(SpectralNormalization, self).__init__(layer, **kwargs)
        
    def build(self, input_shape):
        self.layer.build(input_shape)
        self.w = self.layer.kernel
        self.u = self.add_weight(
            shape=(1, int(self.w.shape[-1])),
            initializer='random_normal',
            trainable=False,
            name='u'
        )
        super(SpectralNormalization, self).build(input_shape)
        
    def call(self, inputs):
        # Power iteration for spectral normalization
        w_reshaped = tf.reshape(self.w, [-1, int(self.w.shape[-1])])
        v = tf.nn.l2_normalize(tf.matmul(self.u, tf.transpose(w_reshaped)))
        u = tf.nn.l2_normalize(tf.matmul(v, w_reshaped))
        
        sigma = tf.matmul(tf.matmul(v, w_reshaped), tf.transpose(u))
        w_norm = self.w / sigma
        
        # Update u
        self.u.assign(u)
        
        # Replace kernel with normalized version
        original_kernel = self.layer.kernel
        self.layer.kernel = w_norm
        output = self.layer(inputs)
        self.layer.kernel = original_kernel
        
        return output

def build_enhanced_cgan_generator(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """
    Build Enhanced CGAN generator with improved architecture:
    - Better label conditioning with FiLM
    - Residual connections
    - Self-attention
    - Progressive upsampling
    """    
    # Noise input
    noise_input = tf.keras.layers.Input(shape=(latent_dim,), name='noise_input')
    
    # Label input with improved embedding
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    label_embedding = tf.keras.layers.Embedding(num_classes, 128)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    label_embedding = tf.keras.layers.Dense(latent_dim, activation='relu')(label_embedding)
    
    # Feature-wise Linear Modulation (FiLM)
    gamma = tf.keras.layers.Dense(latent_dim, activation='sigmoid')(label_embedding)
    beta = tf.keras.layers.Dense(latent_dim)(label_embedding)
    
    # Apply FiLM conditioning
    conditioned_noise = tf.keras.layers.Multiply()([noise_input, gamma])
    conditioned_noise = tf.keras.layers.Add()([conditioned_noise, beta])
    
    # Add residual connection
    combined_input = tf.keras.layers.Add()([noise_input, conditioned_noise])
    
    # Dense layer to create initial feature map
    x = tf.keras.layers.Dense(7 * 7 * 512, use_bias=False)(combined_input)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.Reshape((7, 7, 512))(x)
    x = tf.keras.layers.Dropout(0.2)(x)
    
    # Residual block at 7x7
    residual = x
    x = tf.keras.layers.Conv2D(512, 3, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.Conv2D(512, 3, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Add()([x, residual])
    x = tf.keras.layers.ReLU()(x)
    
    # Self-attention at 7x7 resolution
    # Simplified self-attention
    attention_input = tf.keras.layers.Reshape((49, 512))(x)
    query = tf.keras.layers.Dense(128)(attention_input)
    key = tf.keras.layers.Dense(128)(attention_input)
    value = tf.keras.layers.Dense(128)(attention_input)
    
    # Attention mechanism
    attention_scores = tf.keras.layers.Lambda(
        lambda x: tf.matmul(x[0], x[1], transpose_b=True) / tf.sqrt(128.0)
    )([query, key])
    attention_weights = tf.keras.layers.Activation('softmax')(attention_scores)
    attention_output = tf.keras.layers.Lambda(
        lambda x: tf.matmul(x[0], x[1])
    )([attention_weights, value])
    
    # Project back and add residual
    attention_output = tf.keras.layers.Dense(512)(attention_output)
    attention_output = tf.keras.layers.Add()([attention_input, attention_output])
    x = tf.keras.layers.Reshape((7, 7, 512))(attention_output)
    
    # First upsampling: 7x7x512 -> 14x14x256
    x = tf.keras.layers.Conv2DTranspose(256, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Residual block at 14x14
    residual = x
    x = tf.keras.layers.Conv2D(256, 3, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.Conv2D(256, 3, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Add()([x, residual])
    x = tf.keras.layers.ReLU()(x)
    
    # Second upsampling: 14x14x256 -> 28x28x128
    x = tf.keras.layers.Conv2DTranspose(128, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Final conditioning with label
    label_broadcast = tf.keras.layers.Dense(128)(label_embedding)
    label_broadcast = tf.keras.layers.Reshape((1, 1, 128))(label_broadcast)
    label_broadcast = tf.keras.layers.Lambda(lambda x: tf.tile(x, [1, 28, 28, 1]))(label_broadcast)
    
    # Feature modulation
    x_gamma = tf.keras.layers.Conv2D(128, 1, activation='sigmoid', use_bias=False)(label_broadcast)
    x_beta = tf.keras.layers.Conv2D(128, 1, use_bias=False)(label_broadcast)
    x = tf.keras.layers.Multiply()([x, x_gamma])
    x = tf.keras.layers.Add()([x, x_beta])
    
    # Final layers
    x = tf.keras.layers.Conv2D(64, 3, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    # Output layer
    x = tf.keras.layers.Conv2D(1, 3, padding='same', activation='tanh')(x)
    
    model = tf.keras.Model(
        inputs=[noise_input, label_input],
        outputs=x,
        name='enhanced_cgan_generator'
    )
    
    return model

def build_enhanced_cgan_discriminator(img_height=28, img_width=28, num_classes=16):
    """
    Build Enhanced CGAN discriminator with improved architecture:
    - Spectral normalization
    - Self-attention
    - Better label conditioning
    - Progressive downsampling
    """    
    # Image input
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1), name='img_input')
    label_input = tf.keras.layers.Input(shape=(), dtype='int32', name='label_input')
    
    # Process label with spatial conditioning
    label_embedding = tf.keras.layers.Embedding(num_classes, 64)(label_input)
    label_embedding = tf.keras.layers.Dense(28*28)(label_embedding)
    label_embedding = tf.keras.layers.Reshape((28, 28, 1))(label_embedding)
    
    # Concatenate image and label map
    x = tf.keras.layers.Concatenate()([img_input, label_embedding])
    
    # First conv block: 28x28x2 -> 14x14x64
    x = SpectralNormalization(tf.keras.layers.Conv2D(64, 4, strides=2, padding='same'))(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.2)(x)
    
    # Second conv block: 14x14x64 -> 7x7x128
    x = SpectralNormalization(tf.keras.layers.Conv2D(128, 4, strides=2, padding='same'))(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    # Self-attention at 7x7 resolution
    attention_input = tf.keras.layers.Reshape((49, 128))(x)
    query = tf.keras.layers.Dense(64)(attention_input)
    key = tf.keras.layers.Dense(64)(attention_input)
    value = tf.keras.layers.Dense(64)(attention_input)
    
    # Attention mechanism
    attention_scores = tf.keras.layers.Lambda(
        lambda x: tf.matmul(x[0], x[1], transpose_b=True) / tf.sqrt(64.0)
    )([query, key])
    attention_weights = tf.keras.layers.Activation('softmax')(attention_scores)
    attention_output = tf.keras.layers.Lambda(
        lambda x: tf.matmul(x[0], x[1])
    )([attention_weights, value])
    
    # Project back and add residual
    attention_output = tf.keras.layers.Dense(128)(attention_output)
    attention_output = tf.keras.layers.Add()([attention_input, attention_output])
    x = tf.keras.layers.Reshape((7, 7, 128))(attention_output)
    
    # Third conv block: 7x7x128 -> 4x4x256
    x = SpectralNormalization(tf.keras.layers.Conv2D(256, 4, strides=1, padding='valid'))(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.4)(x)
    
    # Fourth conv block: 4x4x256 -> 2x2x512
    x = SpectralNormalization(tf.keras.layers.Conv2D(512, 3, strides=2, padding='same'))(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Global average pooling
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    
    # Dense layers with better architecture
    x = SpectralNormalization(tf.keras.layers.Dense(512))(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    
    # Separate feature extraction for different tasks
    validity_features = tf.keras.layers.Dense(256, activation='relu')(x)
    class_features = tf.keras.layers.Dense(256, activation='relu')(x)
    
    # Output layers
    validity = tf.keras.layers.Dense(1, activation='sigmoid', name='validity')(validity_features)
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(class_features)
    
    model = tf.keras.Model(
        inputs=[img_input, label_input],
        outputs=[validity, label_pred],
        name='enhanced_cgan_discriminator'
    )
    
    return model

# Build Enhanced CGAN models with improved architecture
enhanced_cgan_generator = build_enhanced_cgan_generator(
    latent_dim=100, 
    num_classes=num_classes, 
    img_height=28, 
    img_width=28
)

enhanced_cgan_discriminator = build_enhanced_cgan_discriminator(
    img_height=28, 
    img_width=28, 
    num_classes=num_classes
)


# Display model architectures
print("\nEnhanced CGAN Generator Architecture:")
enhanced_cgan_generator.summary()

print("\nEnhanced CGAN Discriminator Architecture:")
enhanced_cgan_discriminator.summary()

Enhanced CGAN Generator Architecture:
Model: "enhanced_cgan_generator"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 label_input (InputLayer)       [(None,)]            0           []                               
                                                                                                  
 embedding_16 (Embedding)       (None, 128)          2048        ['label_input[0][0]']            
                                                                                                  
 flatten_25 (Flatten)           (None, 128)          0           ['embedding_16[0][0]']           
                                                                                                  
 dense_20 (Dense)               (None, 100)          12900       ['flatten_25[0][0]']             
                                                                                                  
 noise_input (InputLayer)       [(None, 100)]        0           []                               
                                                                                                  
 dense_21 (Dense)               (None, 100)          10100       ['dense_20[0][0]']               
                                                                                                  
 multiply (Multiply)            (None, 100)          0           ['noise_input[0][0]',            
                                                                  'dense_21[0][0]']               
                                                                                                  
 dense_22 (Dense)               (None, 100)          10100       ['dense_20[0][0]']               
                                                                                                  
 add (Add)                      (None, 100)          0           ['multiply[0][0]',               
                                                                  'dense_22[0][0]']               
                                                                                                  
 add_1 (Add)                    (None, 100)          0           ['noise_input[0][0]',            
                                                                  'add[0][0]']                    
                                                                                                  
 dense_23 (Dense)               (None, 25088)        2508800     ['add_1[0][0]']                  
                                                                                                  
 batch_normalization_140 (Batch  (None, 25088)       100352      ['dense_23[0][0]']               
 Normalization)                                                                                   
                                                                                                  
 re_lu_20 (ReLU)                (None, 25088)        0           ['batch_normalization_140[0][0]']
                                                                                                  
 reshape_10 (Reshape)           (None, 7, 7, 512)    0           ['re_lu_20[0][0]']               
                                                                                                  
 dropout_32 (Dropout)           (None, 7, 7, 512)    0           ['reshape_10[0][0]']             
                                                                                                  
 conv2d_134 (Conv2D)            (None, 7, 7, 512)    2359296     ['dropout_32[0][0]']             
                                                                                                  
 batch_normalization_141 (Batch  (None, 7, 7, 512)   2048        ['conv2d_134[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 re_lu_21 (ReLU)                (None, 7, 7, 512)    0           ['batch_normalization_141[0][0]']
                                                                                                  
 conv2d_135 (Conv2D)            (None, 7, 7, 512)    2359296     ['re_lu_21[0][0]']               
                                                                                                  
 batch_normalization_142 (Batch  (None, 7, 7, 512)   2048        ['conv2d_135[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 add_2 (Add)                    (None, 7, 7, 512)    0           ['batch_normalization_142[0][0]',
                                                                  'dropout_32[0][0]']             
                                                                                                  
 re_lu_22 (ReLU)                (None, 7, 7, 512)    0           ['add_2[0][0]']                  
                                                                                                  
 reshape_11 (Reshape)           (None, 49, 512)      0           ['re_lu_22[0][0]']               
                                                                                                  
 dense_24 (Dense)               (None, 49, 128)      65664       ['reshape_11[0][0]']             
                                                                                                  
 dense_25 (Dense)               (None, 49, 128)      65664       ['reshape_11[0][0]']             
                                                                                                  
 lambda (Lambda)                (None, 49, 49)       0           ['dense_24[0][0]',               
                                                                  'dense_25[0][0]']               
                                                                                                  
 activation_94 (Activation)     (None, 49, 49)       0           ['lambda[0][0]']                 
                                                                                                  
 dense_26 (Dense)               (None, 49, 128)      65664       ['reshape_11[0][0]']             
                                                                                                  
 lambda_1 (Lambda)              (None, 49, 128)      0           ['activation_94[0][0]',          
                                                                  'dense_26[0][0]']               
                                                                                                  
 dense_27 (Dense)               (None, 49, 512)      66048       ['lambda_1[0][0]']               
                                                                                                  
 add_3 (Add)                    (None, 49, 512)      0           ['reshape_11[0][0]',             
                                                                  'dense_27[0][0]']               
                                                                                                  
 reshape_12 (Reshape)           (None, 7, 7, 512)    0           ['add_3[0][0]']                  
                                                                                                  
 conv2d_transpose_26 (Conv2DTra  (None, 14, 14, 256)  3276800    ['reshape_12[0][0]']             
 nspose)                                                                                          
                                                                                                  
 batch_normalization_143 (Batch  (None, 14, 14, 256)  1024       ['conv2d_transpose_26[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 re_lu_23 (ReLU)                (None, 14, 14, 256)  0           ['batch_normalization_143[0][0]']
                                                                                                  
 conv2d_136 (Conv2D)            (None, 14, 14, 256)  589824      ['re_lu_23[0][0]']               
                                                                                                  
 batch_normalization_144 (Batch  (None, 14, 14, 256)  1024       ['conv2d_136[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 re_lu_24 (ReLU)                (None, 14, 14, 256)  0           ['batch_normalization_144[0][0]']
                                                                                                  
 conv2d_137 (Conv2D)            (None, 14, 14, 256)  589824      ['re_lu_24[0][0]']               
                                                                                                  
 batch_normalization_145 (Batch  (None, 14, 14, 256)  1024       ['conv2d_137[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 add_4 (Add)                    (None, 14, 14, 256)  0           ['batch_normalization_145[0][0]',
                                                                  're_lu_23[0][0]']               
                                                                                                  
 re_lu_25 (ReLU)                (None, 14, 14, 256)  0           ['add_4[0][0]']                  
                                                                                                  
 dense_28 (Dense)               (None, 128)          12928       ['dense_20[0][0]']               
                                                                                                  
 conv2d_transpose_27 (Conv2DTra  (None, 28, 28, 128)  819200     ['re_lu_25[0][0]']               
 nspose)                                                                                          
                                                                                                  
 reshape_13 (Reshape)           (None, 1, 1, 128)    0           ['dense_28[0][0]']               
                                                                                                  
 batch_normalization_146 (Batch  (None, 28, 28, 128)  512        ['conv2d_transpose_27[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 lambda_2 (Lambda)              (None, 28, 28, 128)  0           ['reshape_13[0][0]']             
                                                                                                  
 re_lu_26 (ReLU)                (None, 28, 28, 128)  0           ['batch_normalization_146[0][0]']
                                                                                                  
 conv2d_138 (Conv2D)            (None, 28, 28, 128)  16384       ['lambda_2[0][0]']               
                                                                                                  
 multiply_1 (Multiply)          (None, 28, 28, 128)  0           ['re_lu_26[0][0]',               
                                                                  'conv2d_138[0][0]']             
                                                                                                  
 conv2d_139 (Conv2D)            (None, 28, 28, 128)  16384       ['lambda_2[0][0]']               
                                                                                                  
 add_5 (Add)                    (None, 28, 28, 128)  0           ['multiply_1[0][0]',             
                                                                  'conv2d_139[0][0]']             
                                                                                                  
 conv2d_140 (Conv2D)            (None, 28, 28, 64)   73728       ['add_5[0][0]']                  
                                                                                                  
 batch_normalization_147 (Batch  (None, 28, 28, 64)  256         ['conv2d_140[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 re_lu_27 (ReLU)                (None, 28, 28, 64)   0           ['batch_normalization_147[0][0]']
                                                                                                  
 conv2d_141 (Conv2D)            (None, 28, 28, 1)    577         ['re_lu_27[0][0]']               
                                                                                                  
==================================================================================================
Total params: 13,029,517
Trainable params: 12,975,373
Non-trainable params: 54,144
__________________________________________________________________________________________________

Enhanced CGAN Discriminator Architecture:
Model: "enhanced_cgan_discriminator"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 label_input (InputLayer)       [(None,)]            0           []                               
                                                                                                  
 embedding_17 (Embedding)       (None, 64)           1024        ['label_input[0][0]']            
                                                                                                  
 dense_29 (Dense)               (None, 784)          50960       ['embedding_17[0][0]']           
                                                                                                  
 img_input (InputLayer)         [(None, 28, 28, 1)]  0           []                               
                                                                                                  
 reshape_14 (Reshape)           (None, 28, 28, 1)    0           ['dense_29[0][0]']               
                                                                                                  
 concatenate_18 (Concatenate)   (None, 28, 28, 2)    0           ['img_input[0][0]',              
                                                                  'reshape_14[0][0]']             
                                                                                                  
 spectral_normalization_9 (Spec  (None, 14, 14, 64)  2176        ['concatenate_18[0][0]']         
 tralNormalization)                                                                               
                                                                                                  
 leaky_re_lu_42 (LeakyReLU)     (None, 14, 14, 64)   0           ['spectral_normalization_9[0][0]'
                                                                 ]                                
                                                                                                  
 dropout_33 (Dropout)           (None, 14, 14, 64)   0           ['leaky_re_lu_42[0][0]']         
                                                                                                  
 spectral_normalization_10 (Spe  (None, 7, 7, 128)   131328      ['dropout_33[0][0]']             
 ctralNormalization)                                                                              
                                                                                                  
 batch_normalization_148 (Batch  (None, 7, 7, 128)   512         ['spectral_normalization_10[0][0]
 Normalization)                                                  ']                               
                                                                                                  
 leaky_re_lu_43 (LeakyReLU)     (None, 7, 7, 128)    0           ['batch_normalization_148[0][0]']
                                                                                                  
 dropout_34 (Dropout)           (None, 7, 7, 128)    0           ['leaky_re_lu_43[0][0]']         
                                                                                                  
 reshape_15 (Reshape)           (None, 49, 128)      0           ['dropout_34[0][0]']             
                                                                                                  
 dense_30 (Dense)               (None, 49, 64)       8256        ['reshape_15[0][0]']             
                                                                                                  
 dense_31 (Dense)               (None, 49, 64)       8256        ['reshape_15[0][0]']             
                                                                                                  
 lambda_3 (Lambda)              (None, 49, 49)       0           ['dense_30[0][0]',               
                                                                  'dense_31[0][0]']               
                                                                                                  
 activation_95 (Activation)     (None, 49, 49)       0           ['lambda_3[0][0]']               
                                                                                                  
 dense_32 (Dense)               (None, 49, 64)       8256        ['reshape_15[0][0]']             
                                                                                                  
 lambda_4 (Lambda)              (None, 49, 64)       0           ['activation_95[0][0]',          
                                                                  'dense_32[0][0]']               
                                                                                                  
 dense_33 (Dense)               (None, 49, 128)      8320        ['lambda_4[0][0]']               
                                                                                                  
 add_6 (Add)                    (None, 49, 128)      0           ['reshape_15[0][0]',             
                                                                  'dense_33[0][0]']               
                                                                                                  
 reshape_16 (Reshape)           (None, 7, 7, 128)    0           ['add_6[0][0]']                  
                                                                                                  
 spectral_normalization_11 (Spe  (None, 4, 4, 256)   524800      ['reshape_16[0][0]']             
 ctralNormalization)                                                                              
                                                                                                  
 batch_normalization_149 (Batch  (None, 4, 4, 256)   1024        ['spectral_normalization_11[0][0]
 Normalization)                                                  ']                               
                                                                                                  
 leaky_re_lu_44 (LeakyReLU)     (None, 4, 4, 256)    0           ['batch_normalization_149[0][0]']
                                                                                                  
 dropout_35 (Dropout)           (None, 4, 4, 256)    0           ['leaky_re_lu_44[0][0]']         
                                                                                                  
 spectral_normalization_12 (Spe  (None, 2, 2, 512)   1180672     ['dropout_35[0][0]']             
 ctralNormalization)                                                                              
                                                                                                  
 batch_normalization_150 (Batch  (None, 2, 2, 512)   2048        ['spectral_normalization_12[0][0]
 Normalization)                                                  ']                               
                                                                                                  
 leaky_re_lu_45 (LeakyReLU)     (None, 2, 2, 512)    0           ['batch_normalization_150[0][0]']
                                                                                                  
 dropout_36 (Dropout)           (None, 2, 2, 512)    0           ['leaky_re_lu_45[0][0]']         
                                                                                                  
 global_average_pooling2d_1 (Gl  (None, 512)         0           ['dropout_36[0][0]']             
 obalAveragePooling2D)                                                                            
                                                                                                  
 spectral_normalization_13 (Spe  (None, 512)         263168      ['global_average_pooling2d_1[0][0
 ctralNormalization)                                             ]']                              
                                                                                                  
 leaky_re_lu_46 (LeakyReLU)     (None, 512)          0           ['spectral_normalization_13[0][0]
                                                                 ']                               
                                                                                                  
 dropout_37 (Dropout)           (None, 512)          0           ['leaky_re_lu_46[0][0]']         
                                                                                                  
 dense_35 (Dense)               (None, 256)          131328      ['dropout_37[0][0]']             
                                                                                                  
 dense_36 (Dense)               (None, 256)          131328      ['dropout_37[0][0]']             
                                                                                                  
 validity (Dense)               (None, 1)            257         ['dense_35[0][0]']               
                                                                                                  
 label_pred (Dense)             (None, 16)           4112        ['dense_36[0][0]']               
                                                                                                  
==================================================================================================
Total params: 2,457,825
Trainable params: 2,454,561
Non-trainable params: 3,264
__________________________________________________________________________________________________

# =============================================================================
# ENHANCED CGAN TRAINING - COMPLETE 50 EPOCH TRAINING
# =============================================================================

# Test visualization first
print("Testing visualization with current Enhanced CGAN models...")
test_images_enhanced, test_labels_enhanced = display_generated_samples_grid(
    enhanced_cgan_generator, class_to_letter, samples_per_class=6
)

# Training configuration
NUM_EPOCHS = 50


# Set up optimizers and loss functions
enhanced_gen_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
enhanced_disc_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)

enhanced_bce_loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)
enhanced_categorical_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)

# Training history
enhanced_gen_losses = []
enhanced_disc_losses = []
enhanced_label_accuracies = []
enhanced_epochs_list = []

@tf.function
def enhanced_train_step(real_images, real_labels, batch_size):
    # Train discriminator
    noise = tf.random.normal([batch_size, 100])
    fake_labels = tf.random.uniform([batch_size], 0, num_classes, dtype=tf.int32)
    
    with tf.GradientTape() as disc_tape:
        # Generate fake images
        fake_images = enhanced_cgan_generator([noise, fake_labels], training=True)
        
        # Real images
        real_validity, real_label_pred = enhanced_cgan_discriminator([real_images, real_labels], training=True)
        
        # Fake images
        fake_validity, fake_label_pred = enhanced_cgan_discriminator([fake_images, fake_labels], training=True)
        
        # Adversarial losses
        real_loss = enhanced_bce_loss(tf.ones_like(real_validity), real_validity)
        fake_loss = enhanced_bce_loss(tf.zeros_like(fake_validity), fake_validity)
        
        # Label classification losses
        real_label_loss = enhanced_categorical_loss(real_labels, real_label_pred)
        fake_label_loss = enhanced_categorical_loss(fake_labels, fake_label_pred)
        
        # Total discriminator loss
        disc_loss = (real_loss + fake_loss) / 2 + (real_label_loss + fake_label_loss) / 2
    
    # Update discriminator
    gradients = disc_tape.gradient(disc_loss, enhanced_cgan_discriminator.trainable_variables)
    enhanced_disc_optimizer.apply_gradients(zip(gradients, enhanced_cgan_discriminator.trainable_variables))
    
    # Train generator
    noise = tf.random.normal([batch_size, 100])
    fake_labels = tf.random.uniform([batch_size], 0, num_classes, dtype=tf.int32)
    
    with tf.GradientTape() as gen_tape:
        fake_images = enhanced_cgan_generator([noise, fake_labels], training=True)
        fake_validity, fake_label_pred = enhanced_cgan_discriminator([fake_images, fake_labels], training=True)
        
        # Generator wants discriminator to classify fake images as real
        adversarial_loss = enhanced_bce_loss(tf.ones_like(fake_validity), fake_validity)
        
        # Generator wants correct label classification
        label_loss = enhanced_categorical_loss(fake_labels, fake_label_pred)
        
        # Total generator loss
        gen_loss = adversarial_loss + label_loss
    
    # Update generator
    gradients = gen_tape.gradient(gen_loss, enhanced_cgan_generator.trainable_variables)
    enhanced_gen_optimizer.apply_gradients(zip(gradients, enhanced_cgan_generator.trainable_variables))
    
    # Calculate accuracy
    accuracy = tf.keras.metrics.sparse_categorical_accuracy(real_labels, real_label_pred)
    
    return gen_loss, disc_loss, tf.reduce_mean(accuracy)

start_time = time.time()

for epoch in range(NUM_EPOCHS):
    epoch_start = time.time()
    
    print(f"\nEpoch {epoch + 1}")
    print("-" * 60)
    
    # Reset metrics
    epoch_gen_losses = []
    epoch_disc_losses = []
    epoch_accuracies = []
    
    # Training loop
    for step, (real_images, real_labels) in enumerate(train_dataset.take(steps_per_epoch)):
        batch_size = tf.shape(real_images)[0]
        
        gen_loss, disc_loss, accuracy = enhanced_train_step(real_images, real_labels, batch_size)
        
        epoch_gen_losses.append(float(gen_loss))
        epoch_disc_losses.append(float(disc_loss))
        epoch_accuracies.append(float(accuracy))
        
        # Print progress
        if step % 100 == 0:
            print(f"Step {step:4d}/{steps_per_epoch} - "
                  f"Gen Loss: {gen_loss:.4f}, "
                  f"Disc Loss: {disc_loss:.4f}, "
                  f"Accuracy: {accuracy:.4f}")
    
    # Store epoch averages
    avg_gen_loss = np.mean(epoch_gen_losses)
    avg_disc_loss = np.mean(epoch_disc_losses)
    avg_accuracy = np.mean(epoch_accuracies)
    
    enhanced_gen_losses.append(avg_gen_loss)
    enhanced_disc_losses.append(avg_disc_loss)
    enhanced_label_accuracies.append(avg_accuracy)
    enhanced_epochs_list.append(epoch)
    
    # Display only at final epoch
    if (epoch + 1) == NUM_EPOCHS:
        print(f"\nGenerating Grid of Samples at Final Epoch {epoch + 1}:")
        display_generated_samples_grid(enhanced_cgan_generator, class_to_letter, epoch + 1, samples_per_class=6)

    # Calculate and display epoch timing
    epoch_time = time.time() - epoch_start
    total_time = time.time() - start_time
    avg_time = total_time / (epoch + 1)
    eta = avg_time * (NUM_EPOCHS - epoch - 1)
    
total_training_time = time.time() - start_time


# Generate final display
print(f"\nFinal Generated Samples - All Letter Classes:")
final_images_enhanced_cgan, final_labels_enhanced_cgan = display_generated_samples_grid(
    enhanced_cgan_generator, class_to_letter, NUM_EPOCHS, samples_per_class=6
)


# Plot training progress
if len(enhanced_gen_losses) > 1:
    plt.figure(figsize=(15, 5))
    
    # Generator and Discriminator Loss
    plt.subplot(1, 3, 1)
    plt.plot(enhanced_epochs_list, enhanced_gen_losses, label='Generator Loss', color='blue', linewidth=2)
    plt.plot(enhanced_epochs_list, enhanced_disc_losses, label='Discriminator Loss', color='red', linewidth=2)
    plt.title('Enhanced CGAN - Training Losses', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Discriminator Accuracy
    plt.subplot(1, 3, 2)
    plt.plot(enhanced_epochs_list, enhanced_label_accuracies, label='Discriminator Accuracy', color='green', linewidth=2)
    plt.axhline(y=0.95, color='red', linestyle='--', alpha=0.7, label='Upper limit (95%)')
    plt.axhline(y=0.70, color='orange', linestyle='--', alpha=0.7, label='Lower limit (70%)')
    plt.title('Enhanced CGAN - Discriminator Accuracy', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Loss Difference
    plt.subplot(1, 3, 3)
    loss_diff = [g - d for g, d in zip(enhanced_gen_losses, enhanced_disc_losses)]
    plt.plot(enhanced_epochs_list, loss_diff, label='Gen Loss - Disc Loss', color='purple', linewidth=2)
    plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)
    plt.title('Enhanced CGAN - Loss Balance', fontsize=14, fontweight='bold')
    plt.xlabel('Epoch')
    plt.ylabel('Loss Difference')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()

🧪 Testing visualization with current Enhanced CGAN models...
Generating 6 samples per class for all 16 letter classes...

Epoch 1
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.5173, Disc Loss: 3.6070, Accuracy: 0.0312
Step  100/597 - Gen Loss: 3.4003, Disc Loss: 3.1927, Accuracy: 0.4219
Step  200/597 - Gen Loss: 1.7282, Disc Loss: 1.1165, Accuracy: 0.7812
Step  300/597 - Gen Loss: 1.3864, Disc Loss: 0.6041, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.0877, Disc Loss: 0.5931, Accuracy: 1.0000
Step  500/597 - Gen Loss: 0.8605, Disc Loss: 0.7227, Accuracy: 1.0000

Epoch 2
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.1273, Disc Loss: 0.6787, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.2812, Disc Loss: 0.6498, Accuracy: 1.0000
Step  200/597 - Gen Loss: 0.9144, Disc Loss: 0.7598, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.1499, Disc Loss: 0.5584, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.7918, Disc Loss: 0.6809, Accuracy: 0.9688
Step  500/597 - Gen Loss: 1.1205, Disc Loss: 0.6384, Accuracy: 1.0000

Epoch 3
------------------------------------------------------------
Step    0/597 - Gen Loss: 0.7676, Disc Loss: 0.5264, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.1277, Disc Loss: 0.5224, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.5479, Disc Loss: 0.5539, Accuracy: 0.9844
Step  300/597 - Gen Loss: 1.2643, Disc Loss: 0.5518, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.9343, Disc Loss: 0.5396, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.2440, Disc Loss: 0.4949, Accuracy: 1.0000

Epoch 4
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.9701, Disc Loss: 0.4319, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.4767, Disc Loss: 0.4012, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.5421, Disc Loss: 0.4883, Accuracy: 0.9844
Step  300/597 - Gen Loss: 1.6180, Disc Loss: 0.5304, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.0021, Disc Loss: 0.3320, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.9694, Disc Loss: 0.4919, Accuracy: 1.0000

Epoch 5
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.4250, Disc Loss: 0.4925, Accuracy: 0.9844
Step  100/597 - Gen Loss: 1.5523, Disc Loss: 0.5153, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.7143, Disc Loss: 0.5611, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.1574, Disc Loss: 0.4729, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.9389, Disc Loss: 0.5304, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.9665, Disc Loss: 0.3780, Accuracy: 1.0000

Epoch 6
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0207, Disc Loss: 0.3195, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.9198, Disc Loss: 0.6084, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.3163, Disc Loss: 0.6110, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.8953, Disc Loss: 0.3889, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.1240, Disc Loss: 0.3612, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.2095, Disc Loss: 0.3588, Accuracy: 1.0000

Epoch 7
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0291, Disc Loss: 0.4076, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.8896, Disc Loss: 0.7032, Accuracy: 1.0000
Step  200/597 - Gen Loss: 3.0583, Disc Loss: 0.3104, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.6153, Disc Loss: 0.3596, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.3768, Disc Loss: 0.5684, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.8141, Disc Loss: 0.3938, Accuracy: 1.0000

Epoch 8
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.0606, Disc Loss: 0.1862, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.1433, Disc Loss: 0.2174, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.6660, Disc Loss: 0.3517, Accuracy: 1.0000
Step  300/597 - Gen Loss: 3.7734, Disc Loss: 0.4987, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.9956, Disc Loss: 0.4104, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.2333, Disc Loss: 0.3234, Accuracy: 1.0000

Epoch 9
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.6445, Disc Loss: 0.1411, Accuracy: 1.0000
Step  100/597 - Gen Loss: 3.7127, Disc Loss: 0.1337, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.9119, Disc Loss: 0.1522, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.4363, Disc Loss: 0.1667, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.9090, Disc Loss: 0.3113, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.8398, Disc Loss: 0.4852, Accuracy: 1.0000

Epoch 10
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.0539, Disc Loss: 0.3002, Accuracy: 1.0000
Step  100/597 - Gen Loss: 3.0137, Disc Loss: 0.2368, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.2215, Disc Loss: 0.3706, Accuracy: 0.9844
Step  300/597 - Gen Loss: 1.4898, Disc Loss: 0.2763, Accuracy: 0.9844
Step  400/597 - Gen Loss: 3.2008, Disc Loss: 0.5443, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.2398, Disc Loss: 0.4379, Accuracy: 1.0000

Epoch 11
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.6807, Disc Loss: 0.1682, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.5675, Disc Loss: 0.4954, Accuracy: 0.9844
Step  200/597 - Gen Loss: 3.5406, Disc Loss: 0.1341, Accuracy: 0.9844
Step  300/597 - Gen Loss: 3.3687, Disc Loss: 0.1936, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.5326, Disc Loss: 0.2123, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.0379, Disc Loss: 0.1855, Accuracy: 0.9844

Epoch 12
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.5924, Disc Loss: 0.2647, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.4642, Disc Loss: 0.2278, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.7966, Disc Loss: 0.4795, Accuracy: 0.9844
Step  300/597 - Gen Loss: 2.4187, Disc Loss: 0.4431, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.8978, Disc Loss: 0.3571, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.7813, Disc Loss: 0.4986, Accuracy: 1.0000

Epoch 13
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7945, Disc Loss: 0.2973, Accuracy: 0.9844
Step  100/597 - Gen Loss: 0.9426, Disc Loss: 0.4763, Accuracy: 0.9844
Step  200/597 - Gen Loss: 3.9400, Disc Loss: 0.2642, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.8731, Disc Loss: 0.4383, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.0224, Disc Loss: 0.1611, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.6543, Disc Loss: 0.3739, Accuracy: 0.9844

Epoch 14
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.1562, Disc Loss: 0.1876, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.6362, Disc Loss: 0.3381, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.1072, Disc Loss: 0.5863, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.3023, Disc Loss: 0.1837, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.2832, Disc Loss: 0.1110, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.6983, Disc Loss: 0.2084, Accuracy: 1.0000

Epoch 15
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.0222, Disc Loss: 0.1785, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.9287, Disc Loss: 0.5177, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.7636, Disc Loss: 0.2676, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.3188, Disc Loss: 0.2959, Accuracy: 0.9844
Step  400/597 - Gen Loss: 1.8751, Disc Loss: 0.1948, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.4373, Disc Loss: 0.2145, Accuracy: 1.0000

Epoch 16
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.8262, Disc Loss: 0.2547, Accuracy: 0.9844
Step  100/597 - Gen Loss: 1.9564, Disc Loss: 0.3791, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.9443, Disc Loss: 0.2967, Accuracy: 1.0000
Step  300/597 - Gen Loss: 3.0878, Disc Loss: 0.2061, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.6462, Disc Loss: 0.5735, Accuracy: 1.0000
Step  500/597 - Gen Loss: 4.3657, Disc Loss: 0.2535, Accuracy: 1.0000

Epoch 17
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.5600, Disc Loss: 0.2385, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.9906, Disc Loss: 0.2790, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.9574, Disc Loss: 0.1516, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.9709, Disc Loss: 0.5427, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.7090, Disc Loss: 0.1797, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.9163, Disc Loss: 0.1759, Accuracy: 1.0000

Epoch 18
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.0526, Disc Loss: 0.3836, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.9952, Disc Loss: 0.3715, Accuracy: 0.9844
Step  200/597 - Gen Loss: 2.8856, Disc Loss: 0.2652, Accuracy: 0.9688
Step  300/597 - Gen Loss: 2.5462, Disc Loss: 0.6305, Accuracy: 0.9844
Step  400/597 - Gen Loss: 2.7813, Disc Loss: 0.3632, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.4293, Disc Loss: 0.1532, Accuracy: 1.0000

Epoch 19
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.3220, Disc Loss: 0.2379, Accuracy: 0.9844
Step  100/597 - Gen Loss: 3.3764, Disc Loss: 0.3138, Accuracy: 1.0000
Step  200/597 - Gen Loss: 4.0235, Disc Loss: 0.1166, Accuracy: 1.0000
Step  300/597 - Gen Loss: 4.2511, Disc Loss: 0.1766, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.6929, Disc Loss: 0.0693, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.2505, Disc Loss: 0.0954, Accuracy: 1.0000

Epoch 20
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.2605, Disc Loss: 0.0316, Accuracy: 1.0000
Step  100/597 - Gen Loss: 4.4990, Disc Loss: 0.1302, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.1587, Disc Loss: 0.1577, Accuracy: 1.0000
Step  300/597 - Gen Loss: 0.9791, Disc Loss: 0.1687, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.0937, Disc Loss: 0.4438, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.6558, Disc Loss: 0.3267, Accuracy: 1.0000

Epoch 21
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.6431, Disc Loss: 0.5088, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.8833, Disc Loss: 0.4461, Accuracy: 1.0000
Step  200/597 - Gen Loss: 3.0055, Disc Loss: 0.2822, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.9958, Disc Loss: 0.5564, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.8277, Disc Loss: 0.3167, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.0861, Disc Loss: 0.6515, Accuracy: 0.9844

Epoch 22
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.1307, Disc Loss: 0.3331, Accuracy: 1.0000
Step  100/597 - Gen Loss: 3.1153, Disc Loss: 0.3616, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.8228, Disc Loss: 0.2130, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.0607, Disc Loss: 0.2705, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.4425, Disc Loss: 0.3991, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.7961, Disc Loss: 0.4043, Accuracy: 1.0000

Epoch 23
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.6344, Disc Loss: 0.2638, Accuracy: 1.0000
Step  100/597 - Gen Loss: 3.9751, Disc Loss: 0.5934, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.7700, Disc Loss: 0.2411, Accuracy: 1.0000
Step  300/597 - Gen Loss: 5.7474, Disc Loss: 0.3168, Accuracy: 0.9844
Step  400/597 - Gen Loss: 2.0676, Disc Loss: 0.2760, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.4545, Disc Loss: 0.4805, Accuracy: 1.0000

Epoch 24
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7278, Disc Loss: 0.3764, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.2783, Disc Loss: 0.3458, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.3791, Disc Loss: 0.3939, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.7394, Disc Loss: 0.5762, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.0883, Disc Loss: 0.2468, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.8437, Disc Loss: 0.5771, Accuracy: 1.0000

Epoch 25
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.2537, Disc Loss: 0.2439, Accuracy: 1.0000
Step  100/597 - Gen Loss: 4.3684, Disc Loss: 0.1022, Accuracy: 1.0000
Step  200/597 - Gen Loss: 3.8824, Disc Loss: 0.2678, Accuracy: 1.0000
Step  300/597 - Gen Loss: 4.3600, Disc Loss: 0.8350, Accuracy: 1.0000
Step  400/597 - Gen Loss: 6.0195, Disc Loss: 0.2515, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.3182, Disc Loss: 0.1601, Accuracy: 1.0000

Epoch 26
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0395, Disc Loss: 0.3819, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.6228, Disc Loss: 0.2184, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.5952, Disc Loss: 0.5731, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.8860, Disc Loss: 0.1468, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.3555, Disc Loss: 0.1786, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.2297, Disc Loss: 0.4617, Accuracy: 1.0000

Epoch 27
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.9118, Disc Loss: 0.4352, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.8890, Disc Loss: 0.5551, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.4782, Disc Loss: 0.4880, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.1287, Disc Loss: 0.4523, Accuracy: 0.9844
Step  400/597 - Gen Loss: 2.2108, Disc Loss: 0.2902, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0849, Disc Loss: 0.4252, Accuracy: 1.0000

Epoch 28
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.3963, Disc Loss: 0.2859, Accuracy: 1.0000
Step  100/597 - Gen Loss: 0.9649, Disc Loss: 0.4041, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.6064, Disc Loss: 0.3680, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.6205, Disc Loss: 0.5519, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.4772, Disc Loss: 0.4349, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.6317, Disc Loss: 0.3333, Accuracy: 1.0000

Epoch 29
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.0076, Disc Loss: 0.2733, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.8387, Disc Loss: 0.3260, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.6398, Disc Loss: 0.3752, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.7326, Disc Loss: 0.5486, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.2567, Disc Loss: 0.3874, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.7250, Disc Loss: 0.1016, Accuracy: 1.0000

Epoch 30
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.9994, Disc Loss: 0.5309, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.7908, Disc Loss: 0.1697, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.7011, Disc Loss: 0.3897, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.2541, Disc Loss: 0.3925, Accuracy: 1.0000
Step  400/597 - Gen Loss: 0.7985, Disc Loss: 0.2548, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.0496, Disc Loss: 0.3962, Accuracy: 1.0000

Epoch 31
------------------------------------------------------------
Step    0/597 - Gen Loss: 4.4182, Disc Loss: 0.0808, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.4641, Disc Loss: 0.3280, Accuracy: 1.0000
Step  200/597 - Gen Loss: 3.2785, Disc Loss: 0.3043, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.7168, Disc Loss: 0.6540, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.8257, Disc Loss: 0.3662, Accuracy: 1.0000
Step  500/597 - Gen Loss: 0.8125, Disc Loss: 0.5467, Accuracy: 1.0000

Epoch 32
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.6745, Disc Loss: 0.2369, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.9367, Disc Loss: 0.2217, Accuracy: 0.9844
Step  200/597 - Gen Loss: 2.9339, Disc Loss: 0.4855, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.7845, Disc Loss: 0.9495, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.6571, Disc Loss: 0.2359, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.3393, Disc Loss: 0.1455, Accuracy: 1.0000

Epoch 33
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.1996, Disc Loss: 0.3274, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.8820, Disc Loss: 0.4202, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.9712, Disc Loss: 0.0576, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.7701, Disc Loss: 0.2573, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.5117, Disc Loss: 0.4496, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.7163, Disc Loss: 0.3626, Accuracy: 1.0000

Epoch 34
------------------------------------------------------------
Step    0/597 - Gen Loss: 0.9602, Disc Loss: 0.2358, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.1833, Disc Loss: 0.4677, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.6708, Disc Loss: 0.1428, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.7735, Disc Loss: 0.5486, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.4601, Disc Loss: 0.0930, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.3701, Disc Loss: 0.4082, Accuracy: 1.0000

Epoch 35
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.2349, Disc Loss: 0.4788, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.2050, Disc Loss: 0.4480, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.7486, Disc Loss: 0.6385, Accuracy: 0.9844
Step  300/597 - Gen Loss: 2.1610, Disc Loss: 0.3372, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.1572, Disc Loss: 0.8741, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.9842, Disc Loss: 0.2435, Accuracy: 0.9844

Epoch 36
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.6373, Disc Loss: 0.3867, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.5811, Disc Loss: 0.4889, Accuracy: 1.0000
Step  200/597 - Gen Loss: 3.0635, Disc Loss: 0.1913, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.6800, Disc Loss: 0.1602, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.6937, Disc Loss: 0.4480, Accuracy: 0.9844
Step  500/597 - Gen Loss: 3.0612, Disc Loss: 0.4603, Accuracy: 1.0000

Epoch 37
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.9426, Disc Loss: 0.3390, Accuracy: 0.9844
Step  100/597 - Gen Loss: 1.6300, Disc Loss: 0.4150, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.3517, Disc Loss: 0.2819, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.3280, Disc Loss: 0.1478, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.5800, Disc Loss: 0.2448, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.5525, Disc Loss: 0.2101, Accuracy: 1.0000

Epoch 38
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.4270, Disc Loss: 0.5425, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.4207, Disc Loss: 0.3367, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.1398, Disc Loss: 0.4275, Accuracy: 1.0000
Step  300/597 - Gen Loss: 4.0988, Disc Loss: 0.2502, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.2271, Disc Loss: 0.1395, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.6671, Disc Loss: 0.3743, Accuracy: 1.0000

Epoch 39
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.7588, Disc Loss: 0.6776, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.1298, Disc Loss: 0.2406, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.7701, Disc Loss: 0.2405, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.4563, Disc Loss: 0.8833, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.7922, Disc Loss: 0.0941, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.8470, Disc Loss: 0.5225, Accuracy: 1.0000

Epoch 40
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.7720, Disc Loss: 0.2658, Accuracy: 0.9844
Step  100/597 - Gen Loss: 2.2273, Disc Loss: 0.5104, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.5397, Disc Loss: 0.2931, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.4776, Disc Loss: 0.4255, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.1666, Disc Loss: 0.2591, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.7052, Disc Loss: 0.1322, Accuracy: 1.0000

Epoch 41
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.2224, Disc Loss: 0.1575, Accuracy: 1.0000
Step  100/597 - Gen Loss: 3.5117, Disc Loss: 0.5867, Accuracy: 0.9844
Step  200/597 - Gen Loss: 3.6945, Disc Loss: 0.1202, Accuracy: 1.0000
Step  300/597 - Gen Loss: 3.1906, Disc Loss: 0.2600, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.1727, Disc Loss: 0.4048, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.3958, Disc Loss: 0.2649, Accuracy: 1.0000

Epoch 42
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.9941, Disc Loss: 0.2056, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.9017, Disc Loss: 0.2723, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.5867, Disc Loss: 0.2939, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.3508, Disc Loss: 0.3027, Accuracy: 0.9844
Step  400/597 - Gen Loss: 1.7040, Disc Loss: 0.4114, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.1458, Disc Loss: 0.2050, Accuracy: 1.0000

Epoch 43
------------------------------------------------------------
Step    0/597 - Gen Loss: 0.6283, Disc Loss: 0.6908, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.4713, Disc Loss: 0.4245, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.2385, Disc Loss: 0.3767, Accuracy: 1.0000
Step  300/597 - Gen Loss: 1.4906, Disc Loss: 0.4895, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.8334, Disc Loss: 0.3927, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.3340, Disc Loss: 0.3113, Accuracy: 1.0000

Epoch 44
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.4623, Disc Loss: 0.3484, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.5022, Disc Loss: 0.5202, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.6121, Disc Loss: 0.2282, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.3901, Disc Loss: 0.1044, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.4374, Disc Loss: 0.2284, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.6515, Disc Loss: 0.1743, Accuracy: 1.0000

Epoch 45
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.3866, Disc Loss: 0.3291, Accuracy: 1.0000
Step  100/597 - Gen Loss: 4.3039, Disc Loss: 0.1775, Accuracy: 0.9844
Step  200/597 - Gen Loss: 2.4097, Disc Loss: 0.2491, Accuracy: 1.0000
Step  300/597 - Gen Loss: 3.6484, Disc Loss: 0.2165, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.9876, Disc Loss: 0.1182, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.7940, Disc Loss: 0.1993, Accuracy: 1.0000

Epoch 46
------------------------------------------------------------
Step    0/597 - Gen Loss: 3.5496, Disc Loss: 0.2473, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.2197, Disc Loss: 0.6030, Accuracy: 0.9844
Step  200/597 - Gen Loss: 1.5221, Disc Loss: 0.7216, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.4736, Disc Loss: 0.3929, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.8718, Disc Loss: 0.3118, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.8517, Disc Loss: 0.4374, Accuracy: 1.0000

Epoch 47
------------------------------------------------------------
Step    0/597 - Gen Loss: 2.4613, Disc Loss: 0.3245, Accuracy: 1.0000
Step  100/597 - Gen Loss: 1.4967, Disc Loss: 0.3137, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.6631, Disc Loss: 0.4794, Accuracy: 1.0000
Step  300/597 - Gen Loss: 3.5726, Disc Loss: 0.4046, Accuracy: 1.0000
Step  400/597 - Gen Loss: 1.8450, Disc Loss: 0.1957, Accuracy: 0.9844
Step  500/597 - Gen Loss: 2.0811, Disc Loss: 0.5062, Accuracy: 0.9844

Epoch 48
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.2597, Disc Loss: 0.4166, Accuracy: 1.0000
Step  100/597 - Gen Loss: 0.5783, Disc Loss: 0.4595, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.7692, Disc Loss: 0.4126, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.3921, Disc Loss: 0.1368, Accuracy: 1.0000
Step  400/597 - Gen Loss: 2.5431, Disc Loss: 0.5014, Accuracy: 1.0000
Step  500/597 - Gen Loss: 3.2760, Disc Loss: 0.2042, Accuracy: 1.0000

Epoch 49
------------------------------------------------------------
Step    0/597 - Gen Loss: 4.1493, Disc Loss: 0.2174, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.4277, Disc Loss: 0.4108, Accuracy: 1.0000
Step  200/597 - Gen Loss: 1.4193, Disc Loss: 0.3145, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.6866, Disc Loss: 0.1514, Accuracy: 1.0000
Step  400/597 - Gen Loss: 4.2587, Disc Loss: 0.4559, Accuracy: 1.0000
Step  500/597 - Gen Loss: 1.0000, Disc Loss: 0.5030, Accuracy: 1.0000

Epoch 50
------------------------------------------------------------
Step    0/597 - Gen Loss: 1.8456, Disc Loss: 0.3913, Accuracy: 1.0000
Step  100/597 - Gen Loss: 2.1589, Disc Loss: 0.1632, Accuracy: 1.0000
Step  200/597 - Gen Loss: 2.5233, Disc Loss: 0.4711, Accuracy: 1.0000
Step  300/597 - Gen Loss: 2.5479, Disc Loss: 0.8653, Accuracy: 1.0000
Step  400/597 - Gen Loss: 3.4330, Disc Loss: 0.0938, Accuracy: 1.0000
Step  500/597 - Gen Loss: 2.4952, Disc Loss: 0.7561, Accuracy: 0.9844

Generating Grid of Samples at Final Epoch 50:
Generating 6 samples per class for all 16 letter classes...

Final Generated Samples - All Letter Classes:
Generating 6 samples per class for all 16 letter classes...

# =============================================================================
# HYPERPARAMETER TUNING - COMPLETE CGAN EXPERIMENT WITH FID & KL TRACKING
# =============================================================================

# Create output directory
OUTPUT_DIR = "cgan_hyperparameter_results"
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Build CGAN models for hyperparameter tuning (same as original)
def build_cgan_generator_hp(latent_dim=100, num_classes=16, img_height=28, img_width=28):
    """Build CGAN generator identical to original architecture"""
    noise_input = tf.keras.layers.Input(shape=(latent_dim,))
    label_input = tf.keras.layers.Input(shape=(), dtype='int32')
    
    # Label embedding
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Combine noise and label
    combined_input = tf.keras.layers.Concatenate()([noise_input, label_embedding])
    
    # Dense layers
    x = tf.keras.layers.Dense(7 * 7 * 256, use_bias=False)(combined_input)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    x = tf.keras.layers.Reshape((7, 7, 256))(x)
    
    # First upsampling: 7x7 -> 14x14
    x = tf.keras.layers.Conv2DTranspose(128, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    
    # Second upsampling: 14x14 -> 28x28
    x = tf.keras.layers.Conv2DTranspose(64, 5, strides=2, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU()(x)
    
    # Final layer
    output = tf.keras.layers.Conv2DTranspose(1, 5, strides=1, padding='same', use_bias=False, activation='tanh')(x)
    
    return tf.keras.Model([noise_input, label_input], output, name='cgan_generator_hp')

def build_cgan_discriminator_hp(img_height=28, img_width=28, num_classes=16):
    """Build CGAN discriminator identical to original architecture"""
    img_input = tf.keras.layers.Input(shape=(img_height, img_width, 1))
    label_input = tf.keras.layers.Input(shape=(), dtype='int32')
    
    # Image processing
    x = tf.keras.layers.Conv2D(64, 5, strides=2, padding='same')(img_input)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    x = tf.keras.layers.Conv2D(128, 5, strides=2, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    
    x = tf.keras.layers.Flatten()(x)
    
    # Label embedding
    label_embedding = tf.keras.layers.Embedding(num_classes, 50)(label_input)
    label_embedding = tf.keras.layers.Flatten()(label_embedding)
    
    # Combine features
    x = tf.keras.layers.Concatenate()([x, label_embedding])
    
    # Final layers
    x = tf.keras.layers.Dense(1024)(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Dropout(0.5)(x)

    validity = tf.keras.layers.Dense(1, activation='sigmoid', name='validity')(x)
    label_pred = tf.keras.layers.Dense(num_classes, activation='softmax', name='label_pred')(x)
    return tf.keras.Model([img_input, label_input], [validity, label_pred], name='cgan_discriminator_hp')


# ====================== Metric Helpers ======================
def _timestamp():
    return datetime.datetime.now().strftime("%Y%m%d_%H%M%S")

def _to_255_rgb(imgs):
    """imgs: (N,H,W,1) float in [-1,1] or [0,1] or [0,255]; returns uint8 RGB 299x299."""
    x = imgs
    x = np.asarray(x, dtype=np.float32)
    vmin, vmax = np.min(x), np.max(x)
    # map to [0,255]
    if vmax <= 1.0 and vmin >= -1.0:
        # assume [-1,1] or [0,1]
        if vmin >= 0.0:
            x = x * 255.0
        else:
            x = (x + 1.0) * 127.5
    # if already >1, assume [0,255]
    x = np.clip(x, 0.0, 255.0)
    # grayscale -> rgb
    x = np.repeat(x, 3, axis=-1)
    # resize to 299
    x = tf.image.resize(x, (299, 299), method='bilinear').numpy()
    return x.astype(np.float32)

def _inception_activations_uint8(images_uint8_rgb, batch_size=64):
    """images should be float32 0..255 RGB 299x299 - process in batches to avoid OOM"""
    n_images = images_uint8_rgb.shape[0]
    all_features = []
    
    # Process in smaller batches to avoid GPU memory issues
    for i in range(0, n_images, batch_size):
        batch_end = min(i + batch_size, n_images)
        batch_images = images_uint8_rgb[i:batch_end]
        
        x = preprocess_input(batch_images)  # scales to [-1,1]
        batch_feats = INCEPTION(x, training=False).numpy()
        all_features.append(batch_feats)
    
    # Concatenate all batch results
    feats = np.concatenate(all_features, axis=0)
    return feats

def _fid_from_feats(f1, f2, eps=1e-6):
    mu1, mu2 = f1.mean(axis=0), f2.mean(axis=0)
    s1, s2 = np.cov(f1, rowvar=False), np.cov(f2, rowvar=False)
    covmean, _ = sqrtm((s1 @ s2).astype(np.float64), disp=False)
    if not np.isfinite(covmean).all():
        # add a small offset to the diagonal if covs are singular
        offset = np.eye(s1.shape[0]) * eps
        covmean, _ = sqrtm(((s1 + offset) @ (s2 + offset)).astype(np.float64), disp=False)
    # numerical cleanup
    if np.iscomplexobj(covmean):
        covmean = covmean.real
    diff = mu1 - mu2
    fid = diff @ diff + np.trace(s1 + s2 - 2.0 * covmean)
    return float(fid)

def compute_fid(real_imgs, fake_imgs):
    r = _to_255_rgb(real_imgs)
    f = _to_255_rgb(fake_imgs)
    rf = _inception_activations_uint8(r)
    ff = _inception_activations_uint8(f)
    return _fid_from_feats(rf, ff)

def compute_kl_label_divergence(real_labels, gen_label_probs, num_classes):
    """
    KL(P_real || Q_gen), where:
      P_real: empirical distribution of real labels in the sample
      Q_gen: average predicted class probabilities from discriminator on generated images
    """
    # P_real
    counts = np.bincount(real_labels.astype(int), minlength=num_classes).astype(np.float64)
    P = counts / max(1, counts.sum())
    # Q_gen
    Q = np.mean(gen_label_probs, axis=0).astype(np.float64)
    # add eps for stability
    eps = 1e-8
    P = np.clip(P, eps, 1.0)
    Q = np.clip(Q, eps, 1.0)
    return float(np.sum(P * np.log(P / Q)))


# ============== Trainer with per-epoch FID & KL tracking ==============
class CGANHyperparameterTrainer:
    def __init__(self, generator, discriminator, latent_dim=100, num_classes=16, 
                 gen_lr=1e-4, disc_lr=1e-4,
                 metric_real_images=None, metric_real_labels=None, metric_sample_size=256):
        self.generator = generator
        self.discriminator = discriminator
        self.latent_dim = latent_dim
        self.num_classes = num_classes

        self.gen_optimizer = tf.keras.optimizers.Adam(gen_lr)
        self.disc_optimizer = tf.keras.optimizers.Adam(disc_lr)

        self.bce_loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)
        self.ce_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)

        self.gen_loss_metric = tf.keras.metrics.Mean(name='gen_loss')
        self.disc_loss_metric = tf.keras.metrics.Mean(name='disc_loss')
        self.acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(name='label_accuracy')

        self.history = {
            'gen_loss': [], 'disc_loss': [], 'label_accuracy': [],
            'fid': [], 'kl': [], 'epoch': []
        }

        # metric data (fixed subset for reproducibility & speed)
        if metric_real_images is not None and metric_real_labels is not None:
            m = min(metric_sample_size, len(metric_real_images))
            idx = np.random.RandomState(123).choice(len(metric_real_images), size=m, replace=False)
            self.metric_real_images = metric_real_images[idx]
            self.metric_real_labels = metric_real_labels[idx]
        else:
            self.metric_real_images = None
            self.metric_real_labels = None

    @tf.function
    def train_step(self, real_images, real_labels):
        batch_size = tf.shape(real_images)[0]
        # ----- Train D -----
        noise = tf.random.normal([batch_size, self.latent_dim])
        fake_labels = tf.random.uniform([batch_size], minval=0, maxval=self.num_classes, dtype=tf.int32)
        with tf.GradientTape() as tape:
            fake_images = self.generator([noise, fake_labels], training=True)
            real_validity, real_class_logits = self.discriminator([real_images, real_labels], training=True)
            fake_validity, fake_class_logits = self.discriminator([fake_images, fake_labels], training=True)

            real_loss = self.bce_loss(tf.ones_like(real_validity), real_validity)
            fake_loss = self.bce_loss(tf.zeros_like(fake_validity), fake_validity)
            validity_loss = real_loss + fake_loss

            real_class_loss = self.ce_loss(real_labels, real_class_logits)
            fake_class_loss = self.ce_loss(fake_labels, fake_class_logits)
            class_loss = 0.5 * (real_class_loss + fake_class_loss)

            disc_loss = validity_loss + class_loss

        grads = tape.gradient(disc_loss, self.discriminator.trainable_variables)
        self.disc_optimizer.apply_gradients(zip(grads, self.discriminator.trainable_variables))
        self.acc_metric.update_state(real_labels, real_class_logits)

        # ----- Train G -----
        noise = tf.random.normal([batch_size, self.latent_dim])
        gen_labels = tf.random.uniform([batch_size], minval=0, maxval=self.num_classes, dtype=tf.int32)
        with tf.GradientTape() as tape:
            gen_imgs = self.generator([noise, gen_labels], training=True)
            validity, class_logits = self.discriminator([gen_imgs, gen_labels], training=True)
            adv_loss = self.bce_loss(tf.ones_like(validity), validity)
            aux_loss = self.ce_loss(gen_labels, class_logits)
            gen_loss = adv_loss + aux_loss

        grads = tape.gradient(gen_loss, self.generator.trainable_variables)
        self.gen_optimizer.apply_gradients(zip(grads, self.generator.trainable_variables))

        self.gen_loss_metric.update_state(gen_loss)
        self.disc_loss_metric.update_state(disc_loss)

    def _compute_epoch_metrics(self, sample_gen=256):
        if self.metric_real_images is None:
            # metrics disabled if no real sample provided
            self.history['fid'].append(float('nan'))
            self.history['kl'].append(float('nan'))
            return

        m = min(sample_gen, len(self.metric_real_images))
        # sample a fixed slice (first m) for deterministic behavior
        real_imgs = self.metric_real_images[:m]
        real_labels = self.metric_real_labels[:m].astype(np.int32)

        # generate fake images with labels sampled to match real label distribution
        # (fallback to uniform if desired)
        noise = tf.random.normal([m, self.latent_dim])
        fake_labels = tf.convert_to_tensor(real_labels, dtype=tf.int32)
        fake_imgs = self.generator([noise, fake_labels], training=False).numpy()

        # ----- FID -----
        fid = compute_fid(real_imgs, fake_imgs)

        # ----- KL (label distribution): use discriminator predictions on fakes -----
        _, gen_label_probs = self.discriminator([fake_imgs, fake_labels], training=False)
        gen_label_probs = gen_label_probs.numpy()
        kl = compute_kl_label_divergence(real_labels, gen_label_probs, self.num_classes)

        self.history['fid'].append(fid)
        self.history['kl'].append(kl)

    def train_epoch(self, dataset, epoch, steps_per_epoch, verbose=True):
        if verbose:
            print(f"\nEpoch {epoch + 1}")
        self.gen_loss_metric.reset_states()
        self.disc_loss_metric.reset_states()
        self.acc_metric.reset_states()

        for step, (images, labels) in enumerate(dataset.take(steps_per_epoch)):
            self.train_step(images, labels)

        # record losses & accuracy
        self.history['gen_loss'].append(float(self.gen_loss_metric.result()))
        self.history['disc_loss'].append(float(self.disc_loss_metric.result()))
        self.history['label_accuracy'].append(float(self.acc_metric.result()))
        self.history['epoch'].append(epoch)

        # compute per-epoch FID & KL
        self._compute_epoch_metrics()

    def evaluate_performance(self):
        final_gen_loss = self.history['gen_loss'][-1] if self.history['gen_loss'] else float('inf')
        final_disc_loss = self.history['disc_loss'][-1] if self.history['disc_loss'] else float('inf')
        final_accuracy = self.history['label_accuracy'][-1] if self.history['label_accuracy'] else 0.0
        loss_stability = 1 / (1 + np.std(self.history['gen_loss'][-10:] or [0]) + np.std(self.history['disc_loss'][-10:] or [0]))
        score = 0.4 * (1 / (1 + final_gen_loss)) + 0.3 * (1 / (1 + final_disc_loss)) + 0.2 * final_accuracy + 0.1 * loss_stability
        return score


# ============== Visualization & Saving ==============
def save_and_show_generated_grid(generator, num_classes, samples_per_class, experiment_id, class_to_letter=None):
    spc = samples_per_class
    total = num_classes * spc
    rng = tf.random.Generator.from_seed(1234 + int(experiment_id))
    noise = rng.normal(shape=[total, 100])

    labels_list = np.concatenate([np.full(spc, c, dtype=np.int32) for c in range(num_classes)], axis=0)
    labels = tf.constant(labels_list, dtype=tf.int32)

    imgs = generator([noise, labels], training=False)
    imgs = (imgs + 1.0) / 2.0
    imgs = imgs.numpy()

    fig, axes = plt.subplots(spc, num_classes, figsize=(num_classes * 1.6, spc * 1.6))
    fig.suptitle(f"Experiment {experiment_id} — {spc} samples/class", fontsize=14, fontweight='bold')

    for col in range(num_classes):
        header = f"{class_to_letter.get(col, str(col))} (Class {col})" if class_to_letter else f"Class {col}"
        axes[0, col].set_title(header, fontsize=9)
        for row in range(spc):
            idx = col * spc + row
            ax = axes[row, col]
            ax.imshow(imgs[idx, :, :, 0], cmap='gray', vmin=0, vmax=1)
            ax.axis('off')

    plt.tight_layout()
    png_path = os.path.join(OUTPUT_DIR, f"cgan_exp{experiment_id:02d}_samples_{_timestamp()}.png")
    fig.savefig(png_path, dpi=200, bbox_inches='tight')
    plt.show()
    plt.close(fig)
    return png_path

def plot_and_save_metrics(history, experiment_id):
    epochs = np.array(history['epoch'])
    # align metric arrays to same length
    gl = np.array(history['gen_loss'])
    dl = np.array(history['disc_loss'])
    acc = np.array(history['label_accuracy'])
    fid = np.array(history['fid'])
    kl = np.array(history['kl'])

    fig = plt.figure(figsize=(15, 4.5))

    ax1 = plt.subplot(1, 3, 1)
    ax1.plot(epochs, gl, label='Gen Loss', linewidth=2)
    ax1.plot(epochs, dl, label='Disc Loss', linewidth=2)
    ax1.set_title(f'Exp {experiment_id} — Loss per Epoch')
    ax1.set_xlabel('Epoch'); ax1.set_ylabel('Loss'); ax1.legend(); ax1.grid(alpha=0.3)

    ax2 = plt.subplot(1, 3, 2)
    ax2.plot(epochs, fid, label='FID', linewidth=2)
    ax2.set_title(f'Exp {experiment_id} — FID per Epoch')
    ax2.set_xlabel('Epoch'); ax2.set_ylabel('FID'); ax2.grid(alpha=0.3)

    ax3 = plt.subplot(1, 3, 3)
    ax3.plot(epochs, kl, label='KL(P_real || Q_gen)', linewidth=2)
    ax3.set_title(f'Exp {experiment_id} — KL per Epoch')
    ax3.set_xlabel('Epoch'); ax3.set_ylabel('KL'); ax3.grid(alpha=0.3)

    plt.tight_layout()
    metrics_path = os.path.join(OUTPUT_DIR, f"cgan_exp{experiment_id:02d}_metrics_{_timestamp()}.png")
    plt.savefig(metrics_path, dpi=200, bbox_inches='tight')
    plt.show()
    plt.close(fig)
    return metrics_path


# ============== Experiment Runner (now also plots metrics) ==============
def run_hyperparameter_experiment(gen_lr, disc_lr, batch_size, epochs, experiment_id, class_to_letter=None):
    print(f"Running Experiment {experiment_id}: gen_lr={gen_lr}, disc_lr={disc_lr}, batch_size={batch_size}, epochs={epochs}")
    
    generator = build_cgan_generator_hp(latent_dim=100, num_classes=num_classes, img_height=28, img_width=28)
    discriminator = build_cgan_discriminator_hp(img_height=28, img_width=28, num_classes=num_classes)
    
    # Trainer now receives a fixed subset of real data for metrics
    trainer = CGANHyperparameterTrainer(
        generator=generator,
        discriminator=discriminator,
        latent_dim=100,
        num_classes=num_classes,
        gen_lr=gen_lr,
        disc_lr=disc_lr,
        metric_real_images=X_train,          # uses an internal fixed subset for speed
        metric_real_labels=y_train,
        metric_sample_size=256  # Reduced from 1024 to 256 to avoid GPU memory issues
    )
    
    dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
    dataset = dataset.shuffle(BUFFER_SIZE).batch(batch_size, drop_remainder=True).prefetch(tf.data.AUTOTUNE)
    steps_per_epoch = len(X_train) // batch_size
    
    for epoch in range(epochs):
        trainer.train_epoch(dataset, epoch, steps_per_epoch, verbose=False)

    # ---- After last epoch: 10 samples/class (show + save) ----
    grid_path = save_and_show_generated_grid(
        generator=generator,
        num_classes=num_classes,
        samples_per_class=10,
        experiment_id=experiment_id,
        class_to_letter=class_to_letter
    )
    print(f"[Experiment {experiment_id}] Saved sample grid to: {grid_path}")

    # ---- Plot per-epoch Loss, FID, KL (show + save) ----
    metrics_path = plot_and_save_metrics(trainer.history, experiment_id)
    print(f"[Experiment {experiment_id}] Saved metrics plot to: {metrics_path}")

    # ---- Save weights for this experiment ----
    gen_w_path = os.path.join(OUTPUT_DIR, f"cgan_exp{experiment_id:02d}_generator_{_timestamp()}.h5")
    disc_w_path = os.path.join(OUTPUT_DIR, f"cgan_exp{experiment_id:02d}_discriminator_{_timestamp()}.h5")
    generator.save_weights(gen_w_path)
    discriminator.save_weights(disc_w_path)
    print(f"[Experiment {experiment_id}] Saved weights: {gen_w_path}")
    print(f"[Experiment {experiment_id}] Saved weights: {disc_w_path}")
    
    score = trainer.evaluate_performance()
    result = {
        'experiment_id': experiment_id,
        'gen_lr': gen_lr,
        'disc_lr': disc_lr,
        'batch_size': batch_size,
        'epochs': epochs,
        'score': score,
        'final_gen_loss': trainer.history['gen_loss'][-1],
        'final_disc_loss': trainer.history['disc_loss'][-1],
        'final_accuracy': trainer.history['label_accuracy'][-1],
        'generator': generator,
        'discriminator': discriminator,
        'trainer': trainer,
        'grid_path': grid_path,
        'metrics_path': metrics_path,
        'gen_w_path': gen_w_path,
        'disc_w_path': disc_w_path
    }
    print(f"Experiment {experiment_id} completed with score: {score:.4f}")
    return result



# =================== Hyperparameter Sweep ===================
hyperparameter_combinations = [
    (0.0002, 0.0002, 64, 100),
]

results = []
for i, (gen_lr, disc_lr, batch_size, epochs) in enumerate(hyperparameter_combinations, 1):
    result = run_hyperparameter_experiment(gen_lr, disc_lr, batch_size, epochs, i, class_to_letter=class_to_letter)
    results.append(result)

best_result = max(results, key=lambda x: x['score'])
print(f"\nBest hyperparameters:")
print(f"Generator LR: {best_result['gen_lr']}")
print(f"Discriminator LR: {best_result['disc_lr']}")
print(f"Batch Size: {best_result['batch_size']}")
print(f"Score: {best_result['score']:.4f}")

best_generator = best_result['generator']
best_discriminator = best_result['discriminator']

# ============== Save best weights ==============
best_gen_path = os.path.join(OUTPUT_DIR, "best_cgan_generator.h5")
best_disc_path = os.path.join(OUTPUT_DIR, "best_cgan_discriminator.h5")
best_generator.save_weights(best_gen_path)
best_discriminator.save_weights(best_disc_path)
print(f"Saved BEST generator weights to: {best_gen_path}")
print(f"Saved BEST discriminator weights to: {best_disc_path}")

Running Experiment 1: gen_lr=0.0002, disc_lr=0.0002, batch_size=64, epochs=100

[Experiment 1] Saved sample grid to: cgan_hyperparameter_results\cgan_exp01_samples_20250810_053707.png

[Experiment 1] Saved metrics plot to: cgan_hyperparameter_results\cgan_exp01_metrics_20250810_053723.png
[Experiment 1] Saved weights: cgan_hyperparameter_results\cgan_exp01_generator_20250810_053726.h5
[Experiment 1] Saved weights: cgan_hyperparameter_results\cgan_exp01_discriminator_20250810_053726.h5
Experiment 1 completed with score: 0.5934

Best hyperparameters:
Generator LR: 0.0002
Discriminator LR: 0.0002
Batch Size: 64
Score: 0.5934
Saved BEST generator weights to: cgan_hyperparameter_results\best_cgan_generator.h5
Saved BEST discriminator weights to: cgan_hyperparameter_results\best_cgan_discriminator.h5

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Generative Adversarial Networks (GANs) Implementation for EMNIST Letter Generation¶

Part A: Data Analysis and Vanilla GAN Development¶

GAN CA2 PART A¶

AUTHORED BY SHEN LEI AND XAVIER LEE¶

DAAA/FT/2B/22¶

7/14/2025¶

Project Overview¶

Project Objectives¶

Authors and Academic Context¶

Dataset Information¶

Technical Framework¶

1. Environment Setup and Dependencies¶

Library Import Strategy¶

Key Library Categories:¶

Performance Considerations:¶

2. Dataset Import and Initial Analysis¶

EMNIST Letters Dataset Overview¶

Dataset Characteristics:¶

Initial Data Loading Process:¶

Expected Challenges:¶

2.1 Initial Dataset Observations and Data Quality Assessment¶

Key Findings from Initial Analysis:¶

Class Distribution Anomalies¶

Data Quality Indicators¶

Technical Considerations¶

Negative Label Investigation¶

Exploratory Data Analysis & Visualization¶

2.2 Image Orientation Analysis and Correction Strategy¶

Technical Root Cause Analysis¶

Historical Context and Technical Background¶

Observed Transformations¶

Correction Strategy Implementation¶

Impact on GAN Training¶

3. Comprehensive Data Analysis and Feature Engineering¶

3.1 Statistical Analysis and Data Characteristics¶

Purpose and Methodology¶

Analysis Framework¶

3.1.1 Pixel-Level Statistical Analysis¶

3.1.2 Morphological and Structural Analysis¶

3.1.3 Dimensionality Reduction and Visualization¶

Strategic Implications for GAN Design¶

Objectives and what is being achieved¶

Why We Do This¶

3.2 Feature Importance and Pixel Variability Analysis¶

Theoretical Framework¶

Mathematical Foundation¶

Key Insights and Implications¶

Applications to GAN Architecture¶

3.3 Dimensionality Reduction and Cluster Analysis¶

t-distributed Stochastic Neighbor Embedding (t-SNE) Analysis¶

Theoretical Foundation and Mathematical Principles¶

Mathematical Framework¶

Application to EMNIST Letters Dataset¶

Expected Outcomes and Interpretations¶

Strategic Implications for GAN Development¶

Observations from the t-SNE visualization¶

3.4 Class-wise Image Averaging and Prototype Analysis¶

Theoretical Background and Methodology¶

Purpose of Image Averaging Analysis¶

Mathematical Foundation¶

Key Analysis Dimensions¶

Expected Analytical Outcomes¶

Strategic Implications for GAN Development¶

3.4.1 Image Averaging Results and Strategic Insights¶

Quantitative Analysis of Class Coherence¶

High-Variance Classes Identified:¶

Technical Implications of Blurred Averages¶

Strategic Recommendations for GAN Architecture¶

Quality Control and Preprocessing Decisions¶

4. Advanced Data Preprocessing and Feature Engineering¶

4.1 Preprocessing Pipeline Architecture¶

Comprehensive Data Preparation Strategy¶

Core Preprocessing Objectives¶

Advanced Preprocessing Components¶

Integration with GAN Architecture¶

5. Generative Adversarial Network Implementation¶

5.1 GAN Architecture Overview and Training Methodology¶

Theoretical Foundation of GANs¶

Mathematical Framework¶

Training Dynamics and Convergence¶

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